The document discusses enhancing security in data mining through integrating cryptographic and data mining algorithms. It proposes hiding sensitive data within images using a Hill cipher algorithm before storing it in a database. A data mining iterative algorithm would then extract the hidden information from the encrypted images. This approach aims to protect sensitive data from unauthorized access, such as from man-in-the-middle attacks, by only revealing encrypted images even if an attack is successful. The methodology, research design, and results of encrypting data into images and then extracting the information using data mining techniques are described.
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.
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,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
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.
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,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
Data mining over diverse data sources is useful
means for discovering valuable patterns, associations, trends, and
dependencies in data. Many variants of this problem are existing,
depending on how the data is distributed, what type of data
mining we wish to do, how to achieve privacy of data and what
restrictions are placed on sharing of information. A transactional
database owner, lacking in the expertise or computational sources
can outsource its mining tasks to a third party service provider
or server. However, both the itemsets along with the association
rules of the outsourced database are considered private property
of the database owner.
In this paper, we consider a scenario where multiple data sources
are willing to share their data with trusted third party called
combiner who runs data mining algorithms over the union
of their data as long as each data source is guaranteed that
its information that does not pertain to another data source
will not be revealed. The proposed algorithm is characterized
with (1) secret sharing based secure key transfer for distributed
transactional databases with its lightweight encryption is used
for preserving the privacy. (2) and rough set based mechanism
for association rules extraction for an efficient and mining task.
Performance analysis and experimental results are provided for
demonstrating the effectiveness of the proposed algorithm.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
=> Data Mining Services
We are a full service data mining company. We handle projects both large and small, with the help of competent staff which is able to address any of the data mining needs of your company.
- Web Data Mining
- Social Media Data Mining
- SQL Data Mining
- Image Data Mining
- Excel Data Mining
- Word Data Mining
- PDF Data Mining
- Open Source Data Mining
Website: http://datacleaningservices.com/
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
More number of users requires cloud services to transfer private data like electronic health records and financial transaction records. A cloud computing services offers several flavors of virtual machines to handle large scale datasets. But centralized approaches are difficult in handling of large datasets. Data anonymization is used for privacy preservation techniques. It is challenged to manage and process such large-scale data within a cloud application. A scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the Map Reduce framework on cloud. It is used to investigate the scalability problem of large-scale data anonymization techniques. These approaches deliberately design a group of innovative Map Reduce jobs to concretely accomplish the specialization computation in a highly scalable way. The Top-Down Specialization process speeds up the specialization process because indexing structure avoids frequently scanning entire data sets and storing statistical results.
This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.
nternational Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Data mining over diverse data sources is useful
means for discovering valuable patterns, associations, trends, and
dependencies in data. Many variants of this problem are existing,
depending on how the data is distributed, what type of data
mining we wish to do, how to achieve privacy of data and what
restrictions are placed on sharing of information. A transactional
database owner, lacking in the expertise or computational sources
can outsource its mining tasks to a third party service provider
or server. However, both the itemsets along with the association
rules of the outsourced database are considered private property
of the database owner.
In this paper, we consider a scenario where multiple data sources
are willing to share their data with trusted third party called
combiner who runs data mining algorithms over the union
of their data as long as each data source is guaranteed that
its information that does not pertain to another data source
will not be revealed. The proposed algorithm is characterized
with (1) secret sharing based secure key transfer for distributed
transactional databases with its lightweight encryption is used
for preserving the privacy. (2) and rough set based mechanism
for association rules extraction for an efficient and mining task.
Performance analysis and experimental results are provided for
demonstrating the effectiveness of the proposed algorithm.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
=> Data Mining Services
We are a full service data mining company. We handle projects both large and small, with the help of competent staff which is able to address any of the data mining needs of your company.
- Web Data Mining
- Social Media Data Mining
- SQL Data Mining
- Image Data Mining
- Excel Data Mining
- Word Data Mining
- PDF Data Mining
- Open Source Data Mining
Website: http://datacleaningservices.com/
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
More number of users requires cloud services to transfer private data like electronic health records and financial transaction records. A cloud computing services offers several flavors of virtual machines to handle large scale datasets. But centralized approaches are difficult in handling of large datasets. Data anonymization is used for privacy preservation techniques. It is challenged to manage and process such large-scale data within a cloud application. A scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the Map Reduce framework on cloud. It is used to investigate the scalability problem of large-scale data anonymization techniques. These approaches deliberately design a group of innovative Map Reduce jobs to concretely accomplish the specialization computation in a highly scalable way. The Top-Down Specialization process speeds up the specialization process because indexing structure avoids frequently scanning entire data sets and storing statistical results.
This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.
nternational Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
SECURED FREQUENT ITEMSET DISCOVERY IN MULTI PARTY DATA ENVIRONMENT FREQUENT I...Editor IJMTER
Security and privacy methods are used to protect the data values. Private data values are secured with
confidentiality and integrity methods. Privacy model hides the individual identity over the public data values.
Sensitive attributes are protected using anonymity methods. Two or more parties have their own private data under
the distributed environment. The parties can collaborate to calculate any function on the union of their data. Secure
Multiparty Computation (SMC) protocols are used in privacy preserving data mining in distributed environments.
Association rule mining techniques are used to fetch frequent patterns.Apriori algorithm is used to mine association
rules in databases. Homogeneous databases share the same schema but hold information on different entities.
Horizontal partition refers the collection of homogeneous databases that are maintained in different parties. Fast
Distributed Mining (FDM) algorithm is an unsecured distributed version of the Apriori algorithm. Kantarcioglu
and Clifton protocol is used for secure mining of association rules in horizontally distributed databases. Unifying
lists of locally Frequent Itemsets Kantarcioglu and Clifton (UniFI-KC) protocol is used for the rule mining process
in partitioned database environment. UniFI-KC protocol is enhanced in two methods for security enhancement.
Secure computation of threshold function algorithm is used to compute the union of private subsets in each of the
interacting players. Set inclusion computation algorithm is used to test the inclusion of an element held by one
player in a subset held by another.The system is improved to support secure rule mining under vertical partitioned
database environment. The subgroup discovery process is adapted for partitioned database environment. The
system can be improved to support generalized association rule mining process. The system is enhanced to control
security leakages in the rule mining process.
A Survey Paper on an Integrated Approach for Privacy Preserving In High Dimen...IJSRD
Data mining is a technique which is used for extraction of knowledge and information from large amount of data collected by hospitals, government and individuals. The term data mining is also referred as knowledge mining from databases. The major challenge in data mining is ensuring security and privacy of data in databases, because data sharing is common at organizational level. The data in databases comes from a number of sources like – medical, financial, library, marketing, shopping record etc so it is foremost task for anyone to keep secure that data. The objective is to achieve fully privacy preserved data without affecting the data utility in databases. i.e. how data is used or transferred between organizations so that data integrity remains in database but sensitive and confidential data is preserved. This paper presents a brief study about different PPDM techniques like- Randomization, perturbation, Slicing, summarization etc. by use of which the data privacy can be preserved. The technique for which the best computational and theoretical outcome is achieved is chosen for privacy preserving in high dimensional data.
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
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.
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.
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
A Study on Big Data Privacy Protection Models using Data Masking Methods IJECEIAES
In today’s predictive analytics world, data engineering play a vital role, data acquisition is carried out from various source systems and process as per the business applications and domain. Big Data integrates, governs, and secures big data with repeatable, reliable, and maintainable processes. Through volume, speed, and assortment of information characteristics try to reveal business esteem from enormous information. However, with information that is frequently deficient, conflicting, ungoverned, and unprotected, which is hazardous and enormous information being a risk instead of an advantage. What's more, with conventional methodologies that are manual and unpredictable, huge information ventures take too long to acknowledge business esteem. Reasonably and over and again conveying business esteem from enormous information requires another technique. In this connection, raw data has to be moved between onsite and offshore environment during this course of action, data privacy is a major concern and challenge. A Big Data Privacy platform can make it easier to detect, investigate, assess, and remediate threats from intruders. We tried to do complete study of Big Data Privacy using data masking methods on various data loads and different types. This work will help data quality analyst and big data developers while building the big data applications.
In this work we highlighted some of the concepts of data privacy, techniques used in data privacy, and some techniques used in data privacy in the cloud plus some new research trends.
PERFORMING DATA MINING IN (SRMS) THROUGH VERTICAL APPROACH WITH ASSOCIATION R...Editor IJMTER
This system technique is used for efficient data mining in SRMS (Student Records
Management System) through vertical approach with association rules in distributed databases. The
current leading technique is that of Kantarcioglu and Clifton[1]. In this system I deal with two
challenges or issues, one that computes the union of private subsets that each of the interacting users
hold, and another that tests the inclusion of an element held by one user in a subset held by another.
The existing system uses different techniques for data mining purpose like Apriori algorithm. The
Fast Distributed Mining (FDM) algorithm of Cheung et al. [2], which is an unsecured distributed
version of the Apriori algorithm. Proposed system offers enhanced privacy and data mining with
respect to the Encryption techniques and Association rule with Fp-Growth Algorithm in private
cloud (system contains different files of subjects with respect to their branches). Due to this above
techniques the expected effect on this system is that, it is simpler and more efficient in terms of
communication cost and combinational cost. Due to these techniques it will affect the parameter like
time consumption for execution, length of the code is decrease, find the data fast, extracting hidden
predictive information from large databases and the efficiency of this proposed system should
increase by the 20%.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
E04623438
1. IOSR Journal of Engineering (IOSRJEN) www.iosrjen.org
ISSN (e): 2250-3021, ISSN (p): 2278-8719
Vol. 04, Issue 06 (June. 2014), ||V2|| PP 34-38
International organization of Scientific Research 34 | P a g e
To Enhance The Security In Data Mining Using Integration Of
Cryptograhic And Data Mining Algorithms
Gurpreet Kaundal
Student, Lovely Professional University, Phagwara, Pin no. 144402
Abstract: - Data mining is used for to process the information and obtained the knowledge or interesting
patterns from the large amount of data. Data mining is a ground breaking way to leverage the information. The
information that data mining provides can lead to an immense improvement in the quality and dependability of
business decision making. The objective of this work is that to improve the security of the data. Everyday
customers or client’s information is updated, this information is very important data for the clients or the
customers, they never want to disclose with anyone. So, security of information/data is required, needs to
maintain it and also needs to reduce the complexity of data, and to make the sensitive data more reliable
Keywords: - Data Mining, Security, Hill cipher, Attacks.
I. INTRODUCTION
Data mining is the process of discovering interesting patterns from massive amounts of data or
database. A Pattern is interesting or if it valid then easily understood by humans. In data mining knowledge
obtained from patterns which are interested, data mining is a form of knowledge mining [1]. The process of
Knowledge discovery contains the cleaning of data, integration of data, selection of data, and transformation of
data, evaluate the patterns and present the knowledge. Data mining is the innovative way to gaining new and
valuable information. Data mining is a ground breaking way to leverage the information. The information that
data mining provides can lead to an immense improvement in the quality and dependability of business decision
making. Data mining can identify patterns in company data.
II. TOOLS USED IN DATA MINING:
There are different types of data mining tools that are available in marketplace. Most data mining tools can be
classified into three categories:
Traditional Data Mining Tools
Dashboards Data Mining Tools
Text Mining Tools
Traditional Data Mining Tools: The data mining programs helps the many companies to establish their data
patterns. It can be done with the help of complex algorithms and techniques. These tools are installed on the
desktop to monitor the data. It also helps to capture the information, which is residing outside a database.
Dashboards:These tools are installed in computers. It helps to monitor information in database. It also provides
the information regarding data change, and data updates. This functionality makes dashboards easy to use and
particularly appealing to managers who wish to have an overview of the company's performance.
Text Mining Tools: The third type of data mining tool is called text mining tool. It has the ability to mine data
from different kind of text. These tools scan content and convert the selected data into a format that is
compatible with the tool database. Scanned content can be unstructured or structured. Capturing these inputs can
provide organizations with a wealth of information that can be mined to discover trends, concepts, and attitudes.
III. SECURITY IN DATA MINING:
The Security of data is that to protect the data from the any third party which are not authorized. In
simple words the data security is that to protect the data from the unauthorized access of the users. In database
very important data are stored; this data is very useful for making the decisions. Security is a way which is used
to provide protection again loss of data, damage, crime and danger. In the data mining, security is a defined in
the form of protection that provides the conditions which are related with security or improves the security
conditions. The main objective of the security is that to protect the data from the any disaster, corruption and
from the theft, and allows the authorized users to access the information [2]. The system security is that to
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International organization of Scientific Research 35 | P a g e
protect the system or system data from any loss or danger, and allows to users which are authorized access the
data in easy way.
IV. RELATED WORK
Dansana J.et alTools Used in Data Mining:
There are different types of data mining tools that are available in marketplace. Most data mining tools can be
classified into three categories:
Traditional Data Mining Tools
Dashboards Data Mining Tools
Text Mining Tools
Traditional Data Mining Tools: The data mining programs helps the many companies to establish their data
patterns. It can be done with the help of complex algorithms and techniques. These tools are installed on the
desktop to monitor the data. It also helps to capture the information, which is residing outside a database.
Dashboards:These tools are installed in computers. It helps to monitor information in database. It also provides
the information regarding data change, and data updates. This functionality makes dashboards easy to use and
particularly appealing to managers who wish to have an overview of the company's performance.
Text Mining Tools: The third type of data mining tool is called text mining tool. It has the ability to mine data
from different kind of text. These tools scan content and convert the selected data into a format that is
compatible with the tool database. Scanned content can be unstructured or structured. Capturing these inputs can
provide organizations with a wealth of information that can be mined to discover trends, concepts, and attitudes.
(2013)described the privacy preserving of data and also described the data mining decision tree algorithm and
CART algorithm. The CART algorithm is uses as a centralized algorithm; in the distributed environment it is
not used or difficult to use it. In which uses the set of protocols for secure size and sum which are related with
security. In which the author proposed the CART algorithm for security of data in between the multiple parties
in vertically environment [3]. The CART algorithm implements in vertically partitioned environment and also
use the privacy preserving techniques for data leakage. The privacy preserving techniques are used for to
minimize the leakage of data. In data mining process the all attributes of the gini index are securely compute by
using this algorithm. In future many applications of data mining such as medical, academics and library etc. uses
the CART algorithms.
KarthikeswaranD. et al (2012) discussed about the data mining privacy preserving, the data is very
sensitive information or data, every data many transactions occur and every time the transactional data updated,
so need to protect this data from the third party. In which uses the sensitive rule hiding approach for modified
the transactions, most of the cases use the hidden rule (sensitive rule) without generated the false rule [4]. The
author proposed the sensitive rule for modification of transactional data, this sensitive rule hiding processes
more efficient for large databases; rule hiding provides the great impact of the performance. The other issue is
that it not work according to user queries and the user specified constraint. This issue is removing by using the
sensitive rules for hiding, the main goal to build a system which is used to find the sensitive rule for hiding by
the database administrator.
Kaur G. et al(2013) discussed the cryptography scheme to increase the security of data and also
discussed about the information transfer with confidentiality. The cryptography schemes are used to enhance the
security and to protect the data from the security risks. The author proposed the technique for calculating the
PSNR and MSE values with the help of hill cipher with DES for enhance the security. For hiding the text data
behind the image file by using this technique and increase the security [5]. This scheme uses the any image as a
input that are used for calculate the time for text hide or data hide, in which uses the secret text hiding approach,
this is combination of advance hill cipher and the DES techniques for security the data in form of confidential,
secure the data from the unauthorized access. In future, designed the new algorithm using the artificial
intelligence technique for embedding process, this is used for defense research department for ERP systems and
also used for data mining.
V. RESEARCH DESIGN AND RESULTS
RESEARCH DESIGN
To protect the important information first of all need to hide the information so that an attacker can’t
watch it. So store the information by hiding it into images. The information stores inside an image using hill
cipher algorithm and store the image into database. Then data mining iterative algorithm uses for to fetch the
data/information from ciphered image and data. Data mining iterative algorithm is used to discover information
on the basis of iterations. [7] When access the information and retrieve it in form of image, now in this case if
an attacker perform any attack successfully then attacker will take only image because information is hidden
inside the image, only user can discover that information from image which are ciphered. It will make
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information secure. Let’s take example of man-in-middle attack, suppose employees are working in a XYZ Pvt.
Ltd. And they are accessing their accounts information by using data mining from their database, suddenly an
attacker performs man-in-middle attack and attacker discloses all important information.
Fig 1 Attack (man-in-middle)
Here in this Fig 1 shown that user is discovering some information and an attacker is also accessing
same information from path by using man-in-middle attack. In this research work proposes an idea for to
prevent these types of attacks. Stores the information inside an image using hill cipher algorithm and then store
it into database. After that uses the iterative algorithm for data mining to fetch the data/information from
ciphered image and data. An organization wants to mine the data and generates the knowledge from it. This
generated knowledge is important for decision making for an organization. The organizational data may be
present in distributed environment and an organization wants to mine the data at one location, so transfer the
data through the network and collect the data (or information) at one location. In the network the data are not
secure because of security attacks. So, needs to encrypt the data and then transfer the data one location to
another location via network. The organization mines the data and generated the knowledge or extracts the
knowledge from it. This knowledge is very important for an organization in decision making process. After that
uses the data mining iterative algorithm. The Iterative data mining, it will discover information on the basis of
iterations such as Preserving Row and Column Margins, Clustering Preserving Structure and Item set
Frequencies. The information hidden inside the image, the attacker takes the only image and the user can
discover that information from the image, so it will make the information secure as shown in below figure:
Fig 2Data Hiding Inside the Image
The data mining algorithms and techniques are uses for mining the information. Firstly, take the dataset
as input then encrypts the data (or information) by using the hill cipher technique. Then mine the encrypted data
with iterative algorithm and generates the knowledge. This knowledge is most important for an organization in
decision making.
Fig 3 The Research Design
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Fig 4 The Research Design Process
This research work will use the hill cipher algorithm for encrypt the information into the image and
store the image into the database. The hill cipher algorithm contains the plaintext and the key for encrypted the
data into the cipher text, this chiper text is the encrypted data. Firstly, take the dataset as input then uses the Hill
cipher to encrypt information to image hill cipher represent as C= E(K,P) mod 26, as shown in figure 3.4, after
that store the encrypted data into the database in form of image. Then mine the information from the image by
using the data mining algorithm such as iterative algorithm and process the data (or information) and generates
the knowledge from it. This knowledge information is very important information for decision making process
in an organization.
VI. RESULT
Fig 5: Security in data mining
In this case, we upload an image in our database. We write some information in the text field. Which
we wants to encode in the image. When we click on hide data an decoded image is shown. . This coded image is
develpoed by merging image and information. Inside the coded image some information is stored. When
information is stored in an image, its color is changed due to changes in the binary values.
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Fig 6: Data Extraction
Here we can extract the information from the image. In this case, embedded image is browse by the
user. Hence it give three options. In the first case, when we click on show data on text box, it will extract the
information from the iamge and show in the text field. If we click on both, it will also show the information in
text field as well as it will save the data in the file.
REFERENCES
[1] Han, J., and Kamber, M. (2006) Data Mining: Concepts and Techniques, Morgan Kaufmann, India, p. 6-
10, 33-34.
[2] http://blogs.dlt.com/cybersecurity-wednesdays-reduce-cyberpain-restrict-data-access/
[3] Dansana Jayanti, DeyDebadutta and Kumar Raghvendra (2013) “A Novel Approach: CART Algorithm for
Vertically Partitioned Database in Multi-Party Environment”, Proc. of IEEE Conference on Information
and Communication Technologies (ICT), pp. 829-834.
[4] D. Karthikeswarant, V.M. Sudha , V.M. Suresh and A. Javed Sultan (2012) “A Pattern Based
Framework For Privacy Preservation Through Association Rule Mining”, IEEE-International Conference
On Advances In Engineering, Science And Management (ICAESM) , pp.816-821.
[5] Kaur Gurtaptish, MalhotraSheenam (2013) “A Hybrid Approach for Data Hiding using Cryptography
Schemes”, International Journal of Computer Trends and Technology (IJCTT).4(8),pp. 2917-2923..
[6] HeMiao, VittalVijayandZhangJunshan (2013) “Online Dynamic Security Assessment With Missing PMU
Measurements: A Data Mining Approach”, Proc. of IEEE Transaction On Power System.28 (2), pp.
1969-1977
[7] Gurpreet Kaundal, Sheveta Vashisht, Disquisition of a Novel Approach to Enhance Security in Data
Mining, www.ijaret.org Vol. 1, Issue X, Nov.2013 ISSN 2320-6802