3,981 research outputs found
Conditionals in Homomorphic Encryption and Machine Learning Applications
Homomorphic encryption aims at allowing computations on encrypted data
without decryption other than that of the final result. This could provide an
elegant solution to the issue of privacy preservation in data-based
applications, such as those using machine learning, but several open issues
hamper this plan. In this work we assess the possibility for homomorphic
encryption to fully implement its program without relying on other techniques,
such as multiparty computation (SMPC), which may be impossible in many use
cases (for instance due to the high level of communication required). We
proceed in two steps: i) on the basis of the structured program theorem
(Bohm-Jacopini theorem) we identify the relevant minimal set of operations
homomorphic encryption must be able to perform to implement any algorithm; and
ii) we analyse the possibility to solve -- and propose an implementation for --
the most fundamentally relevant issue as it emerges from our analysis, that is,
the implementation of conditionals (requiring comparison and selection/jump
operations). We show how this issue clashes with the fundamental requirements
of homomorphic encryption and could represent a drawback for its use as a
complete solution for privacy preservation in data-based applications, in
particular machine learning ones. Our approach for comparisons is novel and
entirely embedded in homomorphic encryption, while previous studies relied on
other techniques, such as SMPC, demanding high level of communication among
parties, and decryption of intermediate results from data-owners. Our protocol
is also provably safe (sharing the same safety as the homomorphic encryption
schemes), differently from other techniques such as
Order-Preserving/Revealing-Encryption (OPE/ORE).Comment: 14 pages, 1 figure, corrected typos, added introductory pedagogical
section on polynomial approximatio
Secret charing vs. encryption-based techniques for privacy preserving data mining
Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach
Encrypted statistical machine learning: new privacy preserving methods
We present two new statistical machine learning methods designed to learn on
fully homomorphic encrypted (FHE) data. The introduction of FHE schemes
following Gentry (2009) opens up the prospect of privacy preserving statistical
machine learning analysis and modelling of encrypted data without compromising
security constraints. We propose tailored algorithms for applying extremely
random forests, involving a new cryptographic stochastic fraction estimator,
and na\"{i}ve Bayes, involving a semi-parametric model for the class decision
boundary, and show how they can be used to learn and predict from encrypted
data. We demonstrate that these techniques perform competitively on a variety
of classification data sets and provide detailed information about the
computational practicalities of these and other FHE methods.Comment: 39 page
Privacy-Aware Processing of Biometric Templates by Means of Secure Two-Party Computation
The use of biometric data for person identification and access control is gaining more and more popularity. Handling biometric data, however, requires particular care, since biometric data is indissolubly tied to the identity of the owner hence raising important security and privacy issues. This chapter focuses on the latter, presenting an innovative approach that, by relying on tools borrowed from Secure Two Party Computation (STPC) theory, permits to process the biometric data in encrypted form, thus eliminating any risk that private biometric information is leaked during an identification process. The basic concepts behind STPC are reviewed together with the basic cryptographic primitives needed to achieve privacy-aware processing of biometric data in a STPC context. The two main approaches proposed so far, namely homomorphic encryption and garbled circuits, are discussed and the way such techniques can be used to develop a full biometric matching protocol described. Some general guidelines to be used in the design of a privacy-aware biometric system are given, so as to allow the reader to choose the most appropriate tools depending on the application at hand
Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective
Rapid advances in human genomics are enabling researchers to gain a better
understanding of the role of the genome in our health and well-being,
stimulating hope for more effective and cost efficient healthcare. However,
this also prompts a number of security and privacy concerns stemming from the
distinctive characteristics of genomic data. To address them, a new research
community has emerged and produced a large number of publications and
initiatives.
In this paper, we rely on a structured methodology to contextualize and
provide a critical analysis of the current knowledge on privacy-enhancing
technologies used for testing, storing, and sharing genomic data, using a
representative sample of the work published in the past decade. We identify and
discuss limitations, technical challenges, and issues faced by the community,
focusing in particular on those that are inherently tied to the nature of the
problem and are harder for the community alone to address. Finally, we report
on the importance and difficulty of the identified challenges based on an
online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies
(PoPETs), Vol. 2019, Issue
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