Privacy Preserving Data Mining
Yehuda Lindell,Benny Pinkas +1 more
- pp 36-54
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In this paper, the authors introduce the concept of privacy preserving data mining, where two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information.Abstract:
In this paper we introduce the concept of privacy preserving data mining. In our model, two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. This problem has many practical and important applications, such as in medical research with confidential patient records.
Data mining algorithms are usually complex, especially as the size of the input is measured in megabytes, if not gigabytes. A generic secure multi-party computation solution, based on evaluation of a circuit computing the algorithm on the entire input, is therefore of no practical use. We focus on the problem of decision tree learning and use ID3, a popular and widely used algorithm for this problem. We present a solution that is considerably more efficient than generic solutions. It demands very few rounds of communication and reasonable bandwidth. In our solution, each party performs by itself a computation of the same order as computing the ID3 algorithm for its own database. The results are then combined using efficient cryptographic protocols, whose overhead is only logarithmic in the number of transactions in the databases. We feel that our result is a substantial contribution, demonstrating that secure multi-party computation can be made practical, even for complex problems and large inputs.read more
Citations
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Proceedings ArticleDOI
Deep Learning with Differential Privacy
TL;DR: In this paper, the authors develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrate that they can train deep neural networks with nonconvex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
Journal ArticleDOI
Privacy Preserving Data Mining
TL;DR: This work considers a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information, and proposes a protocol that is considerably more efficient than generic solutions and demands both very few rounds of communication and reasonable bandwidth.
Proceedings ArticleDOI
Membership Inference Attacks Against Machine Learning Models
TL;DR: This work quantitatively investigates how machine learning models leak information about the individual data records on which they were trained and empirically evaluates the inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon.
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.
Proceedings ArticleDOI
Deep Learning with Differential Privacy
TL;DR: This work develops new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrates that deep neural networks can be trained with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
References
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Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book ChapterDOI
Public-key cryptosystems based on composite degree residuosity classes
TL;DR: A new trapdoor mechanism is proposed and three encryption schemes are derived : a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA, which are provably secure under appropriate assumptions in the standard model.
Proceedings ArticleDOI
How to play ANY mental game
TL;DR: This work presents a polynomial-time algorithm that, given as a input the description of a game with incomplete information and any number of players, produces a protocol for playing the game that leaks no partial information, provided the majority of the players is honest.
Proceedings ArticleDOI
How to generate and exchange secrets
TL;DR: A new tool for controlling the knowledge transfer process in cryptographic protocol design is introduced and it is applied to solve a general class of problems which include most of the two-party cryptographic problems in the literature.
Proceedings Article
How to Play any Mental Game or A Completeness Theorem for Protocols with Honest Majority
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