PRIVACY PRESERVING DATA MINING BY USING IMPLICIT FUNCTION THEOREMIJNSA Journal
Data mining has made broad significant multidisciplinary field used in vast application domains and extracts knowledge by identifying structural relationship among the objects in large data bases. Privacy preserving data mining is a new area of data mining research for providing privacy of sensitive knowledge of information extracted from data mining system to be shared by the intended persons not to everyone to access. In this paper , we proposed a new approach of privacy preserving data mining by using implicit function theorem for secure transformation of sensitive data obtained from data mining system. we proposed two way enhanced security approach. First transforming original values of sensitive data into different partial derivatives of functional values for perturbation of data. secondly generating symmetric key value by Eigen values of jacobian matrix for secure computation. we given an example of academic sensitive data converting into vector valued functions to explain about our proposed concept and presented implementation based results of new proposed of approach.
An ideal steganographic scheme in networks using twisted payloadeSAT Journals
Abstract With the rapid development of network technology, information security has become a mounting problem. Steganography involves hiding information in a cover media, in such a way that the cover media is not supposed to have any confidential message for its unintentional addressee In this paper, an ideal steganographic scheme in networks is proposed using twisted payload. The confidential image values are twisted by using scrambling techiques.The Discrete Wavelet Transform (DWT) is applied on cover image and Integer Wavelet Transform (IWT) is applied to the scrambled confidential image. Merge operation is done on both images and Inverse DWT is computed on the same to get the stego image. The information hiding algorithm is the reverse process of the extracting algorithm. After this an ideal steganographic scheme is applied which generates a stego image which is immune against conventional attack and performs good perceptibility compared to other steganographic approaches. Index Terms: Network security, Steganography, Discrete Wavelet Transform, Integer Wavelet Transform, Modified Arnold Transform, Merge Operation, Quality Measures
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Additive gaussian noise based data perturbation in multi level trust privacy ...IJDKP
Data perturbation is one of the most popular models used in pr
ivacy preserving data mining. It is specially
convenient for applications where the data owners need to export/publi
sh the privacy-sensitive data. This
work proposes that an Additive Perturbation based Privacy Pre
serving Data Mining (PPDM) to deal with
the problem of increasing accurate models about all data without
knowing exact details of individual
values. To Preserve Privacy, the approach establishes R
andom Perturbation to individual values before
data are published. In Proposed system the PPDM approach introd
uces Multilevel Trust (MLT) on data
miners. Here different perturbed copies of the similar data a
re available to the data miner at different trust
levels and may mingle these copies to jointly gather extra infor
mation about original data and release the
data is called diversity attack. To prevent this attack ML
T-PPDM approach is used along with the addition
of random Gaussian noise and the noise is properly correlated to
the original data, so the data miners
cannot get diversity gain in their combined reconstruction.
A review on privacy preservation in data miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques for masking sensitive information through data modification. The major issues were how to modify the data and how to recover the data mining result from the altered data. The reports were often tightly coupled with the data mining algorithms under consideration. Privacy preserving data publishing focuses on techniques for publishing data, not techniques for data mining. In case, it is expected that standard data mining techniques are applied on the published data. Anonymization of the data is done by hiding the identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data. This survey carries out the various privacy preservation techniques and algorithms.
A Review on Privacy Preservation in Data Miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques
for masking sensitive information through data modification. The major issues were how to modify the data
and how to recover the data mining result from the altered data. The reports were often tightly coupled
with the data mining algorithms under consideration. Privacy preserving data publishing focuses on
techniques for publishing data, not techniques for data mining. In case, it is expected that standard data
mining techniques are applied on the published data. Anonymization of the data is done by hiding the
identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data.
This survey carries out the various privacy preservation techniques and algorithms.
STEGANOGRAPHIC SUBSTITUTION OF THE LEAST SIGNIFICANT BIT DETERMINED THROUGH A...ijcsit
ABSTRACT
The present workproposes to perform an analysis of the similarities between the least significant two bits of the cover image and multiple series of two-bit-length encrypted frames, all of them from the cryptomessage. After finding the most similar frame, we proceed to substitute it into the cover image; nevertheless, to provide a proof of the improvement from using itor the least similar one, the statistics from both cases are obtained.Providing information that the more similar the frame is, the better statistics the stego-image has. Moreover, the statistics obtained from our work are also compared with other works, finding that we provide a good scheme for hiding information.
PRIVACY PRESERVING DATA MINING BY USING IMPLICIT FUNCTION THEOREMIJNSA Journal
Data mining has made broad significant multidisciplinary field used in vast application domains and extracts knowledge by identifying structural relationship among the objects in large data bases. Privacy preserving data mining is a new area of data mining research for providing privacy of sensitive knowledge of information extracted from data mining system to be shared by the intended persons not to everyone to access. In this paper , we proposed a new approach of privacy preserving data mining by using implicit function theorem for secure transformation of sensitive data obtained from data mining system. we proposed two way enhanced security approach. First transforming original values of sensitive data into different partial derivatives of functional values for perturbation of data. secondly generating symmetric key value by Eigen values of jacobian matrix for secure computation. we given an example of academic sensitive data converting into vector valued functions to explain about our proposed concept and presented implementation based results of new proposed of approach.
An ideal steganographic scheme in networks using twisted payloadeSAT Journals
Abstract With the rapid development of network technology, information security has become a mounting problem. Steganography involves hiding information in a cover media, in such a way that the cover media is not supposed to have any confidential message for its unintentional addressee In this paper, an ideal steganographic scheme in networks is proposed using twisted payload. The confidential image values are twisted by using scrambling techiques.The Discrete Wavelet Transform (DWT) is applied on cover image and Integer Wavelet Transform (IWT) is applied to the scrambled confidential image. Merge operation is done on both images and Inverse DWT is computed on the same to get the stego image. The information hiding algorithm is the reverse process of the extracting algorithm. After this an ideal steganographic scheme is applied which generates a stego image which is immune against conventional attack and performs good perceptibility compared to other steganographic approaches. Index Terms: Network security, Steganography, Discrete Wavelet Transform, Integer Wavelet Transform, Modified Arnold Transform, Merge Operation, Quality Measures
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Additive gaussian noise based data perturbation in multi level trust privacy ...IJDKP
Data perturbation is one of the most popular models used in pr
ivacy preserving data mining. It is specially
convenient for applications where the data owners need to export/publi
sh the privacy-sensitive data. This
work proposes that an Additive Perturbation based Privacy Pre
serving Data Mining (PPDM) to deal with
the problem of increasing accurate models about all data without
knowing exact details of individual
values. To Preserve Privacy, the approach establishes R
andom Perturbation to individual values before
data are published. In Proposed system the PPDM approach introd
uces Multilevel Trust (MLT) on data
miners. Here different perturbed copies of the similar data a
re available to the data miner at different trust
levels and may mingle these copies to jointly gather extra infor
mation about original data and release the
data is called diversity attack. To prevent this attack ML
T-PPDM approach is used along with the addition
of random Gaussian noise and the noise is properly correlated to
the original data, so the data miners
cannot get diversity gain in their combined reconstruction.
A review on privacy preservation in data miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques for masking sensitive information through data modification. The major issues were how to modify the data and how to recover the data mining result from the altered data. The reports were often tightly coupled with the data mining algorithms under consideration. Privacy preserving data publishing focuses on techniques for publishing data, not techniques for data mining. In case, it is expected that standard data mining techniques are applied on the published data. Anonymization of the data is done by hiding the identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data. This survey carries out the various privacy preservation techniques and algorithms.
A Review on Privacy Preservation in Data Miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques
for masking sensitive information through data modification. The major issues were how to modify the data
and how to recover the data mining result from the altered data. The reports were often tightly coupled
with the data mining algorithms under consideration. Privacy preserving data publishing focuses on
techniques for publishing data, not techniques for data mining. In case, it is expected that standard data
mining techniques are applied on the published data. Anonymization of the data is done by hiding the
identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data.
This survey carries out the various privacy preservation techniques and algorithms.
STEGANOGRAPHIC SUBSTITUTION OF THE LEAST SIGNIFICANT BIT DETERMINED THROUGH A...ijcsit
ABSTRACT
The present workproposes to perform an analysis of the similarities between the least significant two bits of the cover image and multiple series of two-bit-length encrypted frames, all of them from the cryptomessage. After finding the most similar frame, we proceed to substitute it into the cover image; nevertheless, to provide a proof of the improvement from using itor the least similar one, the statistics from both cases are obtained.Providing information that the more similar the frame is, the better statistics the stego-image has. Moreover, the statistics obtained from our work are also compared with other works, finding that we provide a good scheme for hiding information.
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
An Investigation of Data Privacy and Utility Preservation Using KNN Classific...Kato Mivule
Kato Mivule and Claude Turner, An Investigation of Data Privacy and Utility Preservation Using KNN Classification as a Gauge, International Conference on Information and Knowledge Engineering (IKE 2013), July 22-25, Pages 203-204, Las Vegas, NV, USA
A NOVEL APPROACH FOR CONCEALED DATA SHARING AND DATA EMBEDDING FOR SECURED CO...IJCSEA Journal
This paper introduces a new method of securing image using cryptographic and steganographic techniques. The science of securing a data by encryption is Cryptography whereas the method of hiding secret messages in other essages is Steganography, so that the secret’s very existence is concealed. The term ‘Steganography’ describes the method of hiding cognitive content in another medium to avoid detection by the intruders. The proposed method uses cryptographic and steganographic techniques to encrypt the data as well as hide the encrypted data in another medium so the fact, that a message being sent is concealed. The image is concealed by converting it into a iphertext using SDES algorithm with a secret key,which is also an image, and sent to the receiving end securely.
Text classification based on gated recurrent unit combines with support vecto...IJECEIAES
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
Kato Mivule - Utilizing Noise Addition for Data Privacy, an OverviewKato Mivule
Kato Mivule, "Utilizing Noise Addition for Data Privacy, an Overview", Proceedings of the International Conference on Information and Knowledge Engineering (IKE 2012), Pages 65-71, Las Vegas, NV, USA.
CONTEXT-AWARE CLUSTERING USING GLOVE AND K-MEANSijseajournal
ABSTRACT
In this paper we propose a novel method to cluster categorical data while retaining their context. Typically, clustering is performed on numerical data. However it is often useful to cluster categorical data as well, especially when dealing with data in real-world contexts. Several methods exist which can cluster categorical data, but our approach is unique in that we use recent text-processing and machine learning advancements like GloVe and t- SNE to develop a a context-aware clustering approach (using pre-trained
word embeddings). We encode words or categorical data into numerical, context-aware, vectors that we use to cluster the data points using common clustering algorithms like K-means.
Journal - DATA HIDING SECURITY USING BIT MATCHING-BASED STEGANOGRAPHY AND CR...Budi Prasetiyo
ABSTRACT. This research discussed about the data hiding information using steganography and cryptography. New method are discussed to secure data without change the quality of image as cover medium. Steganographic method is used by find the similarity bit of the message with bit of the MSB (Most Significant Bit) image cover. Finding of similarity process is done by divide and conquer method.The results are bit indexposition, thenthenencrypted using cryptographic. In this paper we using DES (Data Encryption Standard) algorithm. We use data information as message, images, and key as an input. Then, we use our method to secure message. The output is encrypted bit index which containt data hiding information and can be used to secure the messages. To reconstruct the contents, we require the same image and same key. Outcomes of our method can be used to secure the data. The advantages of this method are the capacity of stored data hiding of messages can be larger than the image. The image quality will not change and the capacity of stored messages can be larger than the image. Acoording to the research, both gray scale and colorful images can be used as image cover, except the image contains 100% black and 100% white. Bit matching process on image which have much variety of color takes less time. The damage of messages due to the addition of “salt and pepper” noise starts from 0.00049 of MSE.
A Codon Frequency Obfuscation Heuristic for Raw Genomic Data PrivacyKato Mivule
Genomic data provides clinical researchers with vast opportunities to study various patient ailments. Yet the same data contains revealing information, some of which a patient might want to remain concealed. The question then arises: how can an entity transact in full DNA data while concealing certain sensitive pieces of information in the genome sequence, and maintain DNA data utility? As a response to this question, we propose a codon frequency obfuscation heuristic, in which a redistribution of codon frequency values with highly expressed genes is done in the same amino acid group, generating an obfuscated DNA sequence. Our preliminary results show that it might be possible to publish an obfuscated DNA sequence with a desired level of similarity (utility) to the original DNA sequence. http://arxiv.org/abs/1405.5410
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Towards A Differential Privacy and Utility Preserving Machine Learning Classi...Kato Mivule
Kato Mivule, Claude Turner, Soo-Yeon Ji, "Towards A Differential Privacy and Utility Preserving Machine Learning Classifier", Procedia Computer Science (Complex Adaptive Systems), 2012, Pages 176-181, Washington DC, USA.
A Secure & Optimized Data Hiding Technique Using DWT With PSNR ValueIJERA Editor
Multimedia applications are becoming increasingly significant in modern world. The mushroom growth of multimedia data of these applications, particularly over the web has increased the demand for protection of copyright. Digital watermarking is much more acceptable as a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment. In this paper, a DWT based watermarking scheme is proposed. We have used Genetic Algorithm (GA) in order to make an optimum tradeoff between imperceptibility and robustness by choosing an optimum watermarking level for each coefficient of the cover image. In addition to the suitable watermarking strength, the selection of best block size is also necessary for superior perceptual shaping functions. To achieve this goal we have trained and used GA to pick the best block size to tailor the watermark in one of the coefficients of the DWT. The fitness function criterion for the genetic algorithm decision making is based on PSNR values
AN EFFECTIVE SEMANTIC ENCRYPTED RELATIONAL DATA USING K-NN MODELijsptm
Data exchange and data publishing are becoming an important part of business and academic practices.
Data owners need to maintain the rights over the datasets they share. A right-protection mechanism can be
provided for the ownership of shared data, without revealing its usage under a wide range of machine
learning and mining. In the approach provide two algorithms: the Nearest-Neighbors (NN) and determiner
preserves the Minimum Spanning Tree (MST). The K-NN protocol guarantees that relations between object
remain unaltered. The algorithms preserve the both right protection and utility preservation. The rightprotection
scheme is based on watermarking. Watermarking methodology preserves the distance
relationships.
SELECTIVE ENCRYPTION OF IMAGE BY NUMBER MAZE TECHNIQUEijcisjournal
Due to enormous increase in the usage of computers and mobiles, today’s world is currently flooded with huge volumes of data. This paper is primarily focused on multimedia data and how it can be protected from unwanted attacks. Sharing of multimedia data is easy and very efficient, it has been a customary practice to share multimedia data but there is no proper encryption technique to encrypt multimedia data. Sharing of multimedia data over unprotected networks using DCT algorithm and then applying selective encryption-based algorithm has never been adequately studied. This paper introduces a new selective encryption-based security system which will transfer data with protection even in unauthenticated network. Selective encryption-based security system will also minimize time during encryption process which there by achieves efficiency. The data in the image is transmitted over a network is discriminated using DCT transform and then it will be selectively encrypted using Number Puzzle technique, and thus provides security from unauthorized access. This paper discusses about numeric puzzle-based encryption technique
and how it can achieve security and integrity for multimedia data over traditional encryption technique.
Elite tweets: Analysing the twitter communication patterns of Labour Party Peers in the House of Lords. A session at Twitter and Microblogging: Political, Professional and Personal Practices, Lancaster University 10-12 April 2013 #LUTwit
Ports For Future Offshore Wind-Event Brochuregm330
There are so many considerations when choosing your port & making sure you have the facilities you need to install & then service offshore wind projects. As projects get bigger & parts increase in size & weight, even more questions come to the fore. Join Windpower Monthly’s inaugural Ports For Offshore Wind Forum on 21-22 May 2013 in Aberdeen, Scotland to hear from & network with key representatives throughout the supply chain who are dealing with the logistical challenges faced when preparing your ports. Ensure operations run as smoothly as possible at your harbour to avoid delays & increased costs.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
An Investigation of Data Privacy and Utility Preservation Using KNN Classific...Kato Mivule
Kato Mivule and Claude Turner, An Investigation of Data Privacy and Utility Preservation Using KNN Classification as a Gauge, International Conference on Information and Knowledge Engineering (IKE 2013), July 22-25, Pages 203-204, Las Vegas, NV, USA
A NOVEL APPROACH FOR CONCEALED DATA SHARING AND DATA EMBEDDING FOR SECURED CO...IJCSEA Journal
This paper introduces a new method of securing image using cryptographic and steganographic techniques. The science of securing a data by encryption is Cryptography whereas the method of hiding secret messages in other essages is Steganography, so that the secret’s very existence is concealed. The term ‘Steganography’ describes the method of hiding cognitive content in another medium to avoid detection by the intruders. The proposed method uses cryptographic and steganographic techniques to encrypt the data as well as hide the encrypted data in another medium so the fact, that a message being sent is concealed. The image is concealed by converting it into a iphertext using SDES algorithm with a secret key,which is also an image, and sent to the receiving end securely.
Text classification based on gated recurrent unit combines with support vecto...IJECEIAES
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
Kato Mivule - Utilizing Noise Addition for Data Privacy, an OverviewKato Mivule
Kato Mivule, "Utilizing Noise Addition for Data Privacy, an Overview", Proceedings of the International Conference on Information and Knowledge Engineering (IKE 2012), Pages 65-71, Las Vegas, NV, USA.
CONTEXT-AWARE CLUSTERING USING GLOVE AND K-MEANSijseajournal
ABSTRACT
In this paper we propose a novel method to cluster categorical data while retaining their context. Typically, clustering is performed on numerical data. However it is often useful to cluster categorical data as well, especially when dealing with data in real-world contexts. Several methods exist which can cluster categorical data, but our approach is unique in that we use recent text-processing and machine learning advancements like GloVe and t- SNE to develop a a context-aware clustering approach (using pre-trained
word embeddings). We encode words or categorical data into numerical, context-aware, vectors that we use to cluster the data points using common clustering algorithms like K-means.
Journal - DATA HIDING SECURITY USING BIT MATCHING-BASED STEGANOGRAPHY AND CR...Budi Prasetiyo
ABSTRACT. This research discussed about the data hiding information using steganography and cryptography. New method are discussed to secure data without change the quality of image as cover medium. Steganographic method is used by find the similarity bit of the message with bit of the MSB (Most Significant Bit) image cover. Finding of similarity process is done by divide and conquer method.The results are bit indexposition, thenthenencrypted using cryptographic. In this paper we using DES (Data Encryption Standard) algorithm. We use data information as message, images, and key as an input. Then, we use our method to secure message. The output is encrypted bit index which containt data hiding information and can be used to secure the messages. To reconstruct the contents, we require the same image and same key. Outcomes of our method can be used to secure the data. The advantages of this method are the capacity of stored data hiding of messages can be larger than the image. The image quality will not change and the capacity of stored messages can be larger than the image. Acoording to the research, both gray scale and colorful images can be used as image cover, except the image contains 100% black and 100% white. Bit matching process on image which have much variety of color takes less time. The damage of messages due to the addition of “salt and pepper” noise starts from 0.00049 of MSE.
A Codon Frequency Obfuscation Heuristic for Raw Genomic Data PrivacyKato Mivule
Genomic data provides clinical researchers with vast opportunities to study various patient ailments. Yet the same data contains revealing information, some of which a patient might want to remain concealed. The question then arises: how can an entity transact in full DNA data while concealing certain sensitive pieces of information in the genome sequence, and maintain DNA data utility? As a response to this question, we propose a codon frequency obfuscation heuristic, in which a redistribution of codon frequency values with highly expressed genes is done in the same amino acid group, generating an obfuscated DNA sequence. Our preliminary results show that it might be possible to publish an obfuscated DNA sequence with a desired level of similarity (utility) to the original DNA sequence. http://arxiv.org/abs/1405.5410
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Towards A Differential Privacy and Utility Preserving Machine Learning Classi...Kato Mivule
Kato Mivule, Claude Turner, Soo-Yeon Ji, "Towards A Differential Privacy and Utility Preserving Machine Learning Classifier", Procedia Computer Science (Complex Adaptive Systems), 2012, Pages 176-181, Washington DC, USA.
A Secure & Optimized Data Hiding Technique Using DWT With PSNR ValueIJERA Editor
Multimedia applications are becoming increasingly significant in modern world. The mushroom growth of multimedia data of these applications, particularly over the web has increased the demand for protection of copyright. Digital watermarking is much more acceptable as a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment. In this paper, a DWT based watermarking scheme is proposed. We have used Genetic Algorithm (GA) in order to make an optimum tradeoff between imperceptibility and robustness by choosing an optimum watermarking level for each coefficient of the cover image. In addition to the suitable watermarking strength, the selection of best block size is also necessary for superior perceptual shaping functions. To achieve this goal we have trained and used GA to pick the best block size to tailor the watermark in one of the coefficients of the DWT. The fitness function criterion for the genetic algorithm decision making is based on PSNR values
AN EFFECTIVE SEMANTIC ENCRYPTED RELATIONAL DATA USING K-NN MODELijsptm
Data exchange and data publishing are becoming an important part of business and academic practices.
Data owners need to maintain the rights over the datasets they share. A right-protection mechanism can be
provided for the ownership of shared data, without revealing its usage under a wide range of machine
learning and mining. In the approach provide two algorithms: the Nearest-Neighbors (NN) and determiner
preserves the Minimum Spanning Tree (MST). The K-NN protocol guarantees that relations between object
remain unaltered. The algorithms preserve the both right protection and utility preservation. The rightprotection
scheme is based on watermarking. Watermarking methodology preserves the distance
relationships.
SELECTIVE ENCRYPTION OF IMAGE BY NUMBER MAZE TECHNIQUEijcisjournal
Due to enormous increase in the usage of computers and mobiles, today’s world is currently flooded with huge volumes of data. This paper is primarily focused on multimedia data and how it can be protected from unwanted attacks. Sharing of multimedia data is easy and very efficient, it has been a customary practice to share multimedia data but there is no proper encryption technique to encrypt multimedia data. Sharing of multimedia data over unprotected networks using DCT algorithm and then applying selective encryption-based algorithm has never been adequately studied. This paper introduces a new selective encryption-based security system which will transfer data with protection even in unauthenticated network. Selective encryption-based security system will also minimize time during encryption process which there by achieves efficiency. The data in the image is transmitted over a network is discriminated using DCT transform and then it will be selectively encrypted using Number Puzzle technique, and thus provides security from unauthorized access. This paper discusses about numeric puzzle-based encryption technique
and how it can achieve security and integrity for multimedia data over traditional encryption technique.
Elite tweets: Analysing the twitter communication patterns of Labour Party Peers in the House of Lords. A session at Twitter and Microblogging: Political, Professional and Personal Practices, Lancaster University 10-12 April 2013 #LUTwit
Ports For Future Offshore Wind-Event Brochuregm330
There are so many considerations when choosing your port & making sure you have the facilities you need to install & then service offshore wind projects. As projects get bigger & parts increase in size & weight, even more questions come to the fore. Join Windpower Monthly’s inaugural Ports For Offshore Wind Forum on 21-22 May 2013 in Aberdeen, Scotland to hear from & network with key representatives throughout the supply chain who are dealing with the logistical challenges faced when preparing your ports. Ensure operations run as smoothly as possible at your harbour to avoid delays & increased costs.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
Using Randomized Response Techniques for Privacy-Preserving Data Mining14894
Privacy is an important issue in data mining and knowledge
discovery. In this paper, we propose to use the randomized
response techniques to conduct the data mining computation.
Specially, we present a method to build decision tree
classifiers from the disguised data. We conduct experiments
to compare the accuracy ofou r decision tree with the one
built from the original undisguised data. Our results show
that although the data are disguised, our method can still
achieve fairly high accuracy. We also show how the parameter
used in the randomized response techniques affects the
accuracy ofth e results
Keywords
Privacy, security, decision tree, data mining
https://utilitasmathematica.com/index.php/Index
Our journal has academic and professional communities fosters collaboration and knowledge sharing. When all voices are heard and respected, it strengthens the collective capabilities of the statistical community.
A Review on Privacy Preservation in Data Miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques
for masking sensitive information through data modification. The major issues were how to modify the data
and how to recover the data mining result from the altered data. The reports were often tightly coupled
with the data mining algorithms under consideration. Privacy preserving data publishing focuses on
techniques for publishing data, not techniques for data mining. In case, it is expected that standard data
mining techniques are applied on the published data. Anonymization of the data is done by hiding the
identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data.
This survey carries out the various privacy preservation techniques and algorithms.
A Review on Privacy Preservation in Data Miningijujournal
The main focus of privacy preserving data publishing was to enhance traditional data mining techniques
for masking sensitive information through data modification. The major issues were how to modify the data
and how to recover the data mining result from the altered data. The reports were often tightly coupled
with the data mining algorithms under consideration. Privacy preserving data publishing focuses on
techniques for publishing data, not techniques for data mining. In case, it is expected that standard data
mining techniques are applied on the published data. Anonymization of the data is done by hiding the
identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data.
This survey carries out the various privacy preservation techniques and algorithms.
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
Cluster Based Access Privilege Management Scheme for DatabasesEditor IJMTER
Knowledge discovery is carried out using the data mining techniques. Association rule mining,
classification and clustering operations are carried out under data mining. Clustering method is used to group up the
records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship.
Census data and medical data are referred as micro data. Data publish schemes are used to provide private data for
analysis. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy
preservation process.
Data values are allowed to authorized users using the access control models. Privacy Protection Mechanism
(PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracyconstrained privacy-preserving access control framework is used to manage access control in relational database. The
access control policies define selection predicates available to roles while the privacy requirement is to satisfy the kanonymity or l-diversity. Imprecision bound constraint is assigned for each selection predicate. k-anonymous
Partitioning with Imprecision Bounds (k-PIB) is used to estimate accuracy and privacy constraints. Role-based Access
Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection
Mondrian (TDSM) algorithm is used for query workload-based anonymization. The Top Down Selection Mondrian
(TDSM) algorithm is constructed using greedy heuristics and kd-tree model. Query cuts are selected with minimum
bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the
output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in TopDown Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries.
The privacy preserved access privilege management scheme is enhanced to provide incremental mining
features. Data insert, delete and update operations are connected with the partition management mechanism. Cell level
access control is provided with differential privacy method. Dynamic role management model is integrated with the
access control policy mechanism for query predicates.
An Effective Semantic Encrypted Relational Data Using K-Nn ModelClaraZara1
Data exchange and data publishing are becoming an important part of business and academic practices. Data owners need to maintain the rights over the datasets they share. A right-protection mechanism can be provided for the ownership of shared data, without revealing its usage under a wide range of machine learning and mining. In the approach provide two algorithms: the Nearest-Neighbors (NN) and determiner preserves the Minimum Spanning Tree (MST). The K-NN protocol guarantees that relations between object remain unaltered. The algorithms preserve the both right protection and utility preservation. The right protection scheme is based on watermarking. Watermarking methodology preserves the distance relationships.
Privacy Preserving in Cloud Using Distinctive Elliptic Curve Cryptosystem (DECC)ElavarasaN GanesaN
Securing the data over a cloud network is always a challenging problem
for the researcher over the past one decade. There exist many conventional
algorithms/techniques which proclaim to ensure secure transmission, storage
and retrieval of data over the cloud platform. All these mechanisms mainly
focus on ensuring privacy preserve for of client / user‟s data. This research
work aims to propose distinctive elliptic curve cryptography. DECC is based
on the algebraic structure of elliptic curves in the finite fields. The DECC is
used for privacy preserving since smaller keys are used when compared to all
the rest of the existing cryptographic algorithms. Performance metrics such as
average relative error, time, anonymization time and information loss are
taken into account. Implementations are carried out in MATLAB tool. Results
portrays that the proposed DECC outperforms the existing methods.
Misusability Measure Based Sanitization of Big Data for Privacy Preserving Ma...IJECEIAES
Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy Preserving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Ijnsa050202
1. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
PRIVACY PRESERVING DATA MINING BY USING
IMPLICIT FUNCTION THEOREM
Pasupuleti Rajesh1 and Gugulothu Narsimha2
1
Department of Computer Science and Engineering, VVIT College, Guntur, India
rajesh.pleti@gmail.com
2
Department of Computer Science and Engineering, JNTUH University, Hyderabad,
India
narsimha06@gmail.com
ABSTRACT
Data mining has made broad significant multidisciplinary field used in vast application domains and
extracts knowledge by identifying structural relationship among the objects in large data bases. Privacy
preserving data mining is a new area of data mining research for providing privacy of sensitive knowledge
of information extracted from data mining system to be shared by the intended persons not to everyone to
access. In this paper , we proposed a new approach of privacy preserving data mining by using implicit
function theorem for secure transformation of sensitive data obtained from data mining system. we
proposed two way enhanced security approach. First transforming original values of sensitive data into
different partial derivatives of functional values for perturbation of data. secondly generating symmetric
key value by Eigen values of jacobian matrix for secure computation. we given an example of academic
sensitive data converting into vector valued functions to explain about our proposed concept and
presented implementation based results of new proposed of approach.
KEYWORDS
Data Mining, Implicit Function Theorem, Privacy Preserving, Vector Valued Functions
1. INTRODUCTION
Data mining is considered as one of the most important frontiers in database and information
systems and promising interdisciplinary developments in Information Technology[1]. Data
mining has wide application domain almost in every industry[2]. Data mining has significant
concern in finding the patterns, discovery of knowledge from different business application
domains by applying algorithms like classification, association, clustering etc , to finding future
trends in business to grow. The knowledge extraction services from tremendous volumes of data
has increased greater in vast areas of domains. This type of knowledge may also consisting of
sophisticated sensitive information prevents disclose to access the information by intruders. In
this paper, our objective is to device a symmetric encryption scheme based on implicit function
theorem that enables prescribed privacy guarantees to be proved.
1.1. Privacy Preserving Data Mining
Now a day's information is becoming increasing important and in fact information is a key part
in decision making in an organization. We are in the world of information era. Data is the major
DOI : 10.5121/ijnsa.2013.5202 21
2. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
valuable resource of any enterprise. There is a incredible amount of sensitive data produced by
day-to-day business operational applications. In addition, a major utility of huge databases
today is available from external sources such as market research organizations, independent
surveys and quality testing labs, scientific or economic research . Studies states that the quantity
of data in a certain organization twice every 5 years. Data mining and its integrated fields
efficiently determine valuable sensitive knowledge patterns from large databases, is vulnerable
to exploitation[3], [4]. privacy preserving data mining is an insightful and considered as one of
the most importance basis in database and information systems. An attractive novel trend for
data mining research is the development of techniques that integrates privacy concerns[5], [6],
[7], [8]. The outsourcing of business intelligence data and computing services in acquiring
knowledge sensitive relevance analysis based on privacy preserving data mining
technologies are expected to amenable future trend [9], [10].
1.2. Vector Valued Functions
Vector valued function is a mathematical function of one or more variables whose range is a set
of multidimensional vectors or infinite dimensional vectors.
Suppose is a function from Euclidean n-space to Euclidean m-space. In vector
calculus, the Jacobian matrix is the matrix of all partial derivatives of all these functions (if they
exist) can be structured in an m-by-n matrix, then the Jacobian matrix J of F can be represented
as
The significance of the Jacobian matrix represents the best linear approximation to a
differentiable function near a given point (the point will be consider as given input of sensitive
data). In the sense, the Jacobian is the derivatives of a multivariate function [11].
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3. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
1.3. Implicit Function Theorem
Implicit function theorem is a tool which allows relations to be converted into functions. It does
this by representing the relation as the graph of a function. The conditions guaranteeing that we
can solve form of the variables in terms of p variables along with a formula for computing
derivatives are given by the implicit function theorem [11]. The pairs of x and y which satisfy the
first relationship y=f(x) will also satisfy the second relation .
The motive for calling it an “implicit” function is that it does not say absolute that y depends on
x, but it is a function as the variant of x can vary y as well, in order to maintain the "equals zero"
relation. An implicit function of x and y is simply any association that takes the form
. Any explicit function can be changed into an implicit function using the trick above,
just setting . In theory, any implicit function could be converted into an
explicit function by solving for y in terms of x. In practice, this may be rather challenging, though
it is hard to solve such type of a functions.
The implicit function theorem can be stated as the function has continuous
partials. Suppose and with . The n*n matrix that corresponds to
the y partials of F is invertible. Then there exist a unique function g(x) nearby 'a' such that
[11]. Solving a system of m equations in n unknowns is equivalent to finding
the zeros of a vector-valued function from where n>m. The proposed approach of
privacy preserving data mining by implicit function theorem finds the jacobian matrix of partial
derivatives of such vector valued function represents the best linear approximation of function.
In this paper, we proposed a new approach that facilitate the parties to share the secure
transformation for the real world sensitive information by using implicit function theorem. The
paper is structured as follows. Section 2 describes about Related work. We explained our
proposed approach of Privacy preserving Data mining by using Implicit Function Theorem in
section 3 with an example of academic sensitive information of data. Section 4 provides the
experimental result analysis. Section 5 consists of conclusion and future scope.
2. RELATED WORK
The problem of privacy-preserving data mining has raise to be more significant in recent years for
the reason that of the increasing ability to store personal private data about users and the
increasing sophistication of data mining algorithms to leverage sensitive information of data.
Identification of problems associated to all aspects of privacy and security concerns in data
mining are extensively growing in real time environment applications[7],[12].Two current central
categories to perform data mining tasks without compromising privacy are Perturbation method
and the secure computation method. However, both have a few difficulties, in the first one
reduced accuracy and increased overhead for the second. By including any privacy preserving
technique to data mining, the communication and computation cost will not be increased.
Majority of approaches associated to Privacy-preserving Data mining are
2.1. Anonymization
Sending of multiple records of same type along with original record [13].
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4. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
2.2. Obfuscate
Provide confusion and randomness to sensitive values of data [5], [6], [14], [15], [16].
2.3. Cryptographic Hiding
Proliferation of sensitive data by encryption decryption methods and secure computation methods
[17], [18], [19], [20], [21].
The extracted knowledge obtained from data mining system should not be disclose to everyone.
such analysis of sensitive information has to provide privacy and protect the privacy of
individual sensitive information. Privacy preserving damining framework rationally combines
both features of ambiguity and certainty of original data base and covers diversity of security
parameters need to be consider when outsourcing the sensitive data. In this paper we proposed a
new approach of Privacy preserving Data mining by using Implicit Function Theorem to share
sensitive information of knowledge in secure manner.
3. PROPOSED WORK
Privacy preserving data mining research having two kinds of approaches . The first one is to
changing original data prior to passing through to the data mining system. so that real values of
data are ambiguous(perturbation). The second approach is privacy preserving distributed data
mining(secure computation). our new privacy preserving technique integrates these two
approaches and provides two way enhanced security approach. In this scenario, no party knows
anything excluding its own input and the results. we need some communication between the
parties is required for any interesting computation. but what if the result itself violates privacy?.
We need a techniques to define and quantify privacy to ensure that privacy preserving data
mining results will meet required indented purpose without disclosing sensitive information. Not
revealing of sensitive information , we can ensure the secure communication by applying our
new technique of "Privacy preserving Data mining by using Implicit Function Theorem". The
key idea is to provide secure communication between the parties who wish to share sensitive
extraction of knowledge obtained from data mining system by a symmetric key value of jacobian
matrix.
By employ our new privacy preserving data mining technique using implicit function theorem,
the communication cost to share the sensitive information between parties and computation time
will reduced. By including privacy preserving technique in data mining using implicit function
theorem, the concept of “sensitive information ” cannot be known in advance to intruder because
of dynamically generated secret key value of input data.
A key distinction of privacy preserving data mining by implicit function theorem is to setting not
only the underlying data but also the mined results should be shared for intended persons and
must remain private. This type of proposed approach will solves real-world problems.
Privacy preserving Data mining by using Implicit Function Theorem is two way method
enhanced security approach includes:
1) Perturbation of sensitive knowledge of data by partial derivatives of functional values .
2) Secure computation of key by the eigen value of Jacobian matrix which satisfies the
implicit function theorem
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5. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
The generated secret key value obtained from implicit function theorem takes the input values as
from sensitive kind of data. So the key value is dynamically generated from the type of input of
sensitive selected values of data. The pseudo code of proposed algorithm is
Algorithm 1. Algorithm For Privacy Preserving Data Mining By Using Implicit Function
Theorem
Input: Extracted data mining result or input data base to be shared
Output: Generate dynamic secret key to encrypt and decrypt the data.
Step 1: Identify the sensitive values from the input data
Step 2: Form the sensitive data values into vector valued functions
Step 3: Achieve perturbation by transforming the sensitive values of data into differentiable
functional values by Implicit function theorem.
Step 4: Pass these values to jacobian matrix and generate eigen values
Step 5: Select random eigen secret key value to encrypt and decrypt the sensitive data.
The architecture of the proposed system by Privacy preserving data mining by implicit function is
represented as follows
Sensitive Forming the Transform the original
extraction of functions using values of data into
knowledge from vector valued differentiable functional
Data mining functions values by implicit
system function theorem
Use the Eigen secret Pass these values to
value to encrypt and jacobian matrix and
decrypt the sensitive generate Eigen values
data
Figure 1. proposed architecture for privacy preserving Data mining
let us consider an example of sensitive information of academic details of number of students in
an university according to year wise [22]. our assumed vector valued data set consists of 5
variables and 3 functions. the five variables are Girls(x1), Boys(x2), Total(x3), Placements(x4),
pass percentage(x5) and three functions are Graduation(f1), Post graduation(f2), Total(f3).
Perturbation can be attain by transforming the original values of sensitive data into partial
derivatives of the random functional values. In the example of academic sensitive data, we
applied the following random functions for the year of 2011 and corresponding first order partial
derivative values of sensitive data and sensitive information of university database are
represented in the following way.
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6. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
Table 1: Sensitive information of university data base
, ,
.
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7. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
, ,
.
, ,
Perturbation is achieved by transforming the original values into functional values of the partial
derivatives. Pass these first order partial derivatives as the values in matrix. Then by implicit
function theorem, the derivative of the function F (The Jacobian matrix) represents the best linear
approximation to a differentiable function near a given input points of sensitive information.
Compute the Eigen values of this 3×3 Jacobin matrix are 10610, -810, 7600. Choose one of
the Eigen value as a secrete key to encrypt and decrypt the sensitive original information of data.
In this way, we provided two way enhanced security approach consisting of perturbation and
secure computation for sharing of sensitive information of data among parties.
4. RESULT ANALYSIS
In this section, we assess the performance of the proposed approach privacy preserving data
mining by implicit function theorem. We exploit web server XAMPP 1.8.0 in order to develop
the project of proposed method. XAMPP server contains features like PHP, MYSQL, Tomcat,
Apache, PhPMyAdmin. We experimented with the sensitive information of academic details of
university data according to year of 2011. The meaning is the sensitive
information of number of girls, boys total, placement details and pass percentage of graduate
students in university. First we transform the original sensitive data into the partial derivatives
of the random functional values by PHP code and store the corresponding perturbation values
in excel format. Then export MS excel data to MySQL using the XAMPP phpmyadmin control
panel .
MySQL is the trendy open source database and usually used in combination with the PHP
scripting language to instruct website operations. Then retrieve the perturbation values from
MYSQL data and compute eigen values by pass perturbation values to jacobian matrix with PHP
code. Employ one of the eigen value of jacobian matrix as a symmetric key to encrypt and
decrypt the sensitive information between parties.
Privacy preserving data mining by implicit function theorem is a proliferation, who wish share
the sensitive information among parties by incorporating three components. First, dynamically
generated secret key based on the type of input of sensitive selected values of data. Second,
providing more uncertainty to attacker by transforming the original values of sensitive data into
perturbation values. Third, secure computation of sensitive information with the generated secret
key. The results shows the trade of between the privacy data quality and utility of data. In our
experiment, the instant of time taken for perturbation and secure computation of sensitive values
of data provides a linear approximation time, which represents best fit for the model.
If the intruder tries to find out the communication path or want to modify the message contents,
he cannot achieve, its hardening that because of two way enhanced security of approach and
dynamically generated secret key value based on sensitive input data. The computational cost of
encryption and decryption is linear and reasonable as it is varies along with the amount of data to
be send. The following graph shows time complexity based analysis to encrypt and decrypt
sensitive information of data in seconds for various kinds of data sizes in KB's.
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8. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
Figure 2. Time execution analysis for various data sizes
The output of the proposed method executed by XAMPP web server is showed in the following
figure.
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9. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
Figure 3. Output of the proposed approach
Privacy preserving
S. Differential K-
Characteristics by implicit function Cryptography
No privacy anonymization
theorem
Dynamically
generated based on Fixed/
1 Security key Fixed Fixed
type of input Dynamic
sensitive data
Interface of third
2 No Yes Yes Yes
parties
High
3 Accuracy (Two Way Enhanced High High Low
approach)
Computational
4 Low High High Low
Complexity
Table 2 : A comparison of proposed algorithm.
The above table provides technical features of various privacy preserving techniques in data
mining. A common characteristic of most of the previously studied frameworks is that patterns
mined from huge volumes of data may be anonymized, otherwise transformed, altered to
perturbation, encryption/decryption of sensitive data and secure multi party encryption schemas.
In our proposed approach of privacy preserving data mining by implicit function theorem, we
proposed two way enhanced approach includes both perturbation and encryption in a linear
approximation time.
privacy preserving data mining describes to avoid information disclosure due to legitimate
admission to the data. Sensitive knowledge which can be extract from databases by by means of
data mining algorithms to be excluded, since such knowledge can evenly well compromise
information privacy.
The proposed approach of privacy preserving data mining by implicit function theorem protects
the sensitive information of a data with enhanced accuracy and without loss of information which
creates a model for usability of data. The sensitive information of data can also be reconstructed.
With a amount of well exposed and expensive thefts creating both remarkable legal liability and
bad publicity for the effected industries , business has rapidly developed more sophisticated in
defending against such attacks, but so have the hackers.
There is a emergent requirement to protect sensitive information of industry data, members of
staff information, client information across the enterprise wherever such data may reside. Until in
recent times, mainly data theft take place from malicious individuals hacking into production
databases.
privacy preserving data mining by implicit function theorem provides the tradeoff between
privacy and utility problem by considering the privacy and algorithmic requirements at the same
time.
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10. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
Privacy Preserving Data Mining (PPDM) tackles the problem of developing precise models
about summarized data without access to exact information in personal individual sensitive data
information. A broadly studied perturbation-based privacy preserving data mining and
cryptography schema approaches introduced more confusion to adversary to obtain sensitive
information there by reducing chance of vulnerability to preserve privacy prior to data are
published.
5. CONCLUSION AND FUTURE SCOPE
Privacy preserving of information is the stage key role, because of outstanding to progress in
technology, contribution of organizational precise data and usefulness of information has
enlarged immensely. National and iternational business corporations spends billions of dollars for
protecting the senstive information. Generally, many of vulnerabilities come up with the financial
servicies and government organizations. Proposed algorithm of privacy preserving data mining by
implicit function theorem is two way enhanced security approach can be employed for various
real time application domains. Sensitive information of privacy preserving data mining will be
present in the forms of analysis information similar to Medicine - hospital cost analysis,
prediction hospital cost analysis, drug side effects, automotive diagnostic expert systems genetic
sequence analysis. Finance - credit assessment, fraud detection stock market prediction,
Marketing/sales - sales prediction, product analysis, target mailing, identifying unusual
behaviour, buying patterns, Scientific discovery, Knowledge Acquisition. In addition to that,
privacy preserving data mining by implicit function theorem kind of approach will also be used
in distributed data mining to protect information of privacy and applied for business data, which
can be represented in the form of vetor valued functions.
ACKNOWLEDGEMENTS
The authors would like to extend their gratitude to the anonymous reviewers and who
continuously supported to bring out this technical paper. I would like to thank my advisor, Dr.
A. Damodaram, Director of Academic Audit Cell, Jawaharlal Nehru Technological
University Hyderabad , for his professional assistance and remarkable insights. I would
like to express my gratitude to Prof. G. Narsimha for helping me take the first steps in
the research area. And finally, special thanks to my mother and father for providing the
moral support to me. we are pleased to acknowledge our sincere thanks to our beloved
chairman sri.V. Vidya Sagar and Director prof. S.R.K. Paramahamsa for valuable
cooperation.
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Author
P.Rajesh received the M.Tech degree in computer science and engineering (CSE) from
Jawaharlal Nehru Technological University Hyderabad in 2009. He is currently pursuing Ph.
D degree in the department of computer science and engineering from Jawaharlal Nehru
Technological University Hyderabad and working as an assistant professor in CSE
department at Vasireddy Venkatadri Institute of technology, Guntur, Andhra Pradesh. His
research interests are in the area of Data mining, Information security, Privacy preserving data
publishing and sharing
Dr.G.Narshima received Ph.D degree from osmania university, Hyderabad. He is having
thirteen years of teaching experience and having seven years of research experience in
various prestigious institutions. He is currently working as an associate professor in the
department of computer science and engineering from Jawaharlal Nehru Technological
University Hyderabad. He has enormous research and teaching learning experience in
various prestigious universities. His research interests are in databases, data privacy, Data
mining, Information security, Information networks, Mobile communications, Image processing, Privacy
preserving data publishing and sharing.
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