Open AccessJournal Article
Privacy preserving association rule mining in vertically partitioned data
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TLDR
A privacy preserving association rule mining algorithm was introduced that preserved privacy of individual values by computing scalar product and the security was analyzed.Abstract:
A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzedread more
Citations
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Proceedings ArticleDOI
Privacy-Preserving Deep Learning
Reza Shokri,Vitaly Shmatikov +1 more
TL;DR: This paper presents a practical system that enables multiple parties to jointly learn an accurate neural-network model for a given objective without sharing their input datasets, and exploits the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously.
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A survey on trust management for Internet of Things
TL;DR: This paper investigates the properties of trust, proposes objectives of IoT trust management, and provides a survey on the current literature advances towards trustworthy IoT to propose a research model for holistic trust management in IoT.
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Privacy-preserving distributed mining of association rules on horizontally partitioned data
Murat Kantarcioglu,Chris Clifton +1 more
TL;DR: In this paper, the authors address secure mining of association rules over horizontally partitioned data. And they incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.
Journal ArticleDOI
State-of-the-art in privacy preserving data mining
Vassilios S. Verykios,Elisa Bertino,Igor Nai Fovino,Loredana Parasiliti Provenza,Yucel Saygin,Yannis Theodoridis +5 more
TL;DR: An overview of the new and rapidly emerging research area of privacy preserving data mining is provided, and a classification hierarchy that sets the basis for analyzing the work which has been performed in this context is proposed.
Proceedings Article
On k -anonymity and the curse of dimensionality
TL;DR: It is shown that the curse of high dimensionality also applies to the problem of privacy preserving data mining, and when a data set contains a large number of attributes which are open to inference attacks, it becomes difficult to anonymize the data without an unacceptably high amount of information loss.
References
More filters
Proceedings ArticleDOI
Privacy-Preserving Deep Learning
Reza Shokri,Vitaly Shmatikov +1 more
TL;DR: This paper presents a practical system that enables multiple parties to jointly learn an accurate neural-network model for a given objective without sharing their input datasets, and exploits the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously.
Journal ArticleDOI
A survey on trust management for Internet of Things
TL;DR: This paper investigates the properties of trust, proposes objectives of IoT trust management, and provides a survey on the current literature advances towards trustworthy IoT to propose a research model for holistic trust management in IoT.
Journal ArticleDOI
Privacy-preserving distributed mining of association rules on horizontally partitioned data
Murat Kantarcioglu,Chris Clifton +1 more
TL;DR: In this paper, the authors address secure mining of association rules over horizontally partitioned data. And they incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.
Journal ArticleDOI
State-of-the-art in privacy preserving data mining
Vassilios S. Verykios,Elisa Bertino,Igor Nai Fovino,Loredana Parasiliti Provenza,Yucel Saygin,Yannis Theodoridis +5 more
TL;DR: An overview of the new and rapidly emerging research area of privacy preserving data mining is provided, and a classification hierarchy that sets the basis for analyzing the work which has been performed in this context is proposed.
Proceedings Article
On k -anonymity and the curse of dimensionality
TL;DR: It is shown that the curse of high dimensionality also applies to the problem of privacy preserving data mining, and when a data set contains a large number of attributes which are open to inference attacks, it becomes difficult to anonymize the data without an unacceptably high amount of information loss.
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