Privacy Preserving DB Systems
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Privacy Perserving DataBases, how they are managed, built and secured. with an introduction to main methods of Anonymization techniques, PPDB data mining, P3P and Hippocratic DBs.
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Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
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Privacy Preserving Data Mining
Every person involved,is concerned about the leakage of private data i.e privacy of the individual's data.Today privacy of data is one of the most serious concerns which people face on an individual as well as organisational level and it has to be dealt with in an effective
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Privacy preserving in data mining with hybrid approach
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Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
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it was my first seminar topic, wasn't that good presentation though...
A Review Study on the Privacy Preserving Data Mining Techniques and Approaches
In this paper we review on the
various privacy preserving data mining techniques like data
modification and secure multiparty computation based on the
different aspects.
Index Terms– Privacy and Security, Data Mining, Privacy
Preserving, Secure Multiparty Computation (SMC) and Data
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Every person involved,is concerned about the leakage of private data i.e privacy of the individual's data.Today privacy of data is one of the most serious concerns which people face on an individual as well as organisational level and it has to be dealt with in an effective
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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.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Mining
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 Mining
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
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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.
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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.
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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.A review on privacy preservation in data mining
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.
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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.
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In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned
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use their own data records (a subset of the overall data) in addition to the external background knowledge to
infer the data records contributed by other data providers. The paper addresses this new threat and makes several
contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a
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anonymized data with efficiency. Experiments on real-life datasets suggest that our approach achieves better or
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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.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Mining
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 Mining
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
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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
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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
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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.
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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.
Privacy Preserving Distributed Association Rule Mining Algorithm for Vertical...
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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.A review on privacy preservation in data mining
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.
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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.
Multilevel Privacy Preserving by Linear and Non Linear Data Distortion
http://iosrjournals.org/iosr-jce/pages/v14i5.html
www.ijerd.com
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.
Ej24856861
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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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
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In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned
data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may
use their own data records (a subset of the overall data) in addition to the external background knowledge to
infer the data records contributed by other data providers. The paper addresses this new threat and makes several
contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a
given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic
algorithms exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering
techniques for efficiently checking m-privacy given a set of records. Finally, we present a data provider-aware
anonymization algorithm with adaptive m- privacy checking strategies to ensure high utility and m-privacy of
anonymized data with efficiency. Experiments on real-life datasets suggest that our approach achieves better or
comparable utility and efficiency than existing and baseline algorithms while providing m-privacy guarantee.
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Hands On Trove: Database as a Service in OpenStack
Hands On Trove: Database as a Service in OpenStack
An introduction to database as-a-service with open stack trove
An introduction to database as-a-service with open stack trove
Similar to Privacy Preserving DB Systems
Preparing research data for sharing
workshop session delivered alongside 'Making your thesis legal' workshop in July and September 2013 to PhD, MPhil, DrPh students who are completing their thesis. Discusses standards for sharing data, issues that need addressing, formats, data protection, usability, licenses
Creating a Data Management Plan
Webinar delivered by the Research Medical Library at the University of Texas MD Anderson Cancer Center on 11/12/15.
Preparing Research Data for Sharing
A description of the issues that need to be considered when considering sharing of research data
Data Management Planning - 02/21/13
These are the slides from the Data Management Planning Class, taught 2/21/13 at the Georgia Tech Library.
NIH Data Policy or: How I Learned to Stop Worrying and Love the Data Manageme...
This talk provides background information on the NIH policy, what it is and how it came to be. It then goes through how to create a data sharing plan on your own and using the DMPTool. The talk wraps up with my top 5 recommendations for data management for those who have never done data management before.
Data attribute security and privacy in Collaborative distributed database Pub...
Data attribute security and privacy in Collaborative distributed database Pub...International Journal of Engineering Inventions www.ijeijournal.com
In this era, there are need to secure data in distributed database system. For collaborative data
publishing some anonymization techniques are available such as generalization and bucketization. We consider
the attack can call as “insider attack” by colluding data providers who may use their own records to infer
others records. To protect our database from these types of attacks we used slicing technique for anonymization,
as above techniques are not suitable for high dimensional data. It cause loss of data and also they need clear
separation of quasi identifier and sensitive database. We consider this threat and make several contributions.
First, we introduce a notion of data privacy and used slicing technique which shows that anonymized data
satisfies privacy and security of data which classifies data vertically and horizontally. Second, we present
verification algorithms which prove the security against number of providers of data and insure high utility and
data privacy of anonymized data with efficiency. For experimental result we use the hospital patient datasets
and suggest that our slicing approach achieves better or comparable utility and efficiency than baseline
algorithms while satisfying data security. Our experiment successfully demonstrates the difference between
computation time of encryption algorithm which is used to secure data and our system.
Securing, storing and enabling safe access to data
Invited talk as part of Westminster Insight Research Data Management Forum, https://www.westminsterinsight.co.uk/event/3416/Research_Data_Management_Forum
Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers
Our presentation introduces the GDPR then gives a brief overview of key GDPR principles and explains how they are likely to affect data scientists and behavioral researchers.
A Review on Privacy Preservation in Data Mining
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 Mining
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.
Privacy Preserving Data Mining Using Inverse Frequent ItemSet Mining Approach
https://irjet.net/archives/V4/i2/IRJET-V4I2173.pdf
Data Management Lab: Data management plan instructions
Spring 2014 Data Management Lab: Session 1 Data management plan instructions
OpenAIRE webinar on Open Research Data in H2020 (OAW2016)
Presentation by Marjan Grootveld (DANS) on the Open Research Data in H2020 - OpenAIRE webinar Oct. 26 (OAW2016)
Similar to Privacy Preserving DB Systems (20)
NIH Data Policy or: How I Learned to Stop Worrying and Love the Data Manageme...
NIH Data Policy or: How I Learned to Stop Worrying and Love the Data Manageme...
Data attribute security and privacy in Collaborative distributed database Pub...
Data attribute security and privacy in Collaborative distributed database Pub...
Securing, storing and enabling safe access to data
Securing, storing and enabling safe access to data
Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers
Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers
Privacy Preserving Data Mining Using Inverse Frequent ItemSet Mining Approach
Privacy Preserving Data Mining Using Inverse Frequent ItemSet Mining Approach
Data Management Lab: Data management plan instructions
Data Management Lab: Data management plan instructions
OpenAIRE webinar on Open Research Data in H2020 (OAW2016)
OpenAIRE webinar on Open Research Data in H2020 (OAW2016)
Recently uploaded
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
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.
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Epistemic Interaction - tuning interfaces to provide information for AI support
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.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
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.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Pushing the limits of ePRTC: 100ns holdover for 100 days
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.
By Design, not by Accident - Agile Venture Bolzano 2024
As presented at the Agile Venture Bolzano, 4.06.2024
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GridMate - End to end testing is a critical piece to ensure quality and avoid...
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
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/albumentationsRecently uploaded (20)
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
By Design, not by Accident - Agile Venture Bolzano 2024
By Design, not by Accident - Agile Venture Bolzano 2024
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Privacy Preserving DB Systems
- 1. Privacy-Preserving Database Systems Presented By: Ashraf Bashir
- 13. P3P (cont’d)
- 14. P3P (cont’d)
- 15. P3P (cont’d)
- 16. P3P (cont’d)
- 17. P3P (cont’d)
- 18. P3P (cont’d)
- 19. P3P (cont’d)
- 20. P3P (cont’d) Is the scenario acceptable as privacy ?
- 21. P3P (cont’d)
- 22. P3P (cont’d) Reasonable questions: 1- is passing data through safe zone reasonable ? 2- Does the user need to accept the policy manually ?
- 23. P3P (cont’d)
- 24. P3P (cont’d)
- 38. Questions ?