We present a framework GenoPPML for privacy-preserving machine learning in the context of sensitive genomic data processing. The technology combines secure multiparty computation techniques based on the recently proposed Manticore framework for model training and fully homomorphic encryption based on TFHE for model inference. The framework was successfully used to solve breast cancer prediction problems on gene expression datasets coming from distinct private sources while preserving their privacy - the solution winning 1st place for both Tracks I and III of the genomic privacy competition iDASH’2020. Extensive benchmarks and comparisons to existing works are performed. Our 2-party logistic regression computation is 11× faster than the one in [1] on the same dataset and it uses only one CPU core.

International audience

International audience

Abstract Classification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities they provide. Among the tools that may be used to ensure such privacy, we are focusing in this paper on functional encryption. These relatively new cryptographic primitives enable the evaluation of functions over encrypted inputs, outputting cleartext results. Theoretically, this property makes them well-suited to process classification over encrypted data in a privacy by design’ rationale, enabling to perform the classification algorithm over encrypted inputs (i.e. without knowing the inputs) while only getting the input classes as a result in the clear. In this paper, we study the security and privacy issues of classifiers using today practical functional encryption schemes. We provide an analysis of the information leakage about the input data that are processed in the encrypted domain with state-of-the-art functional encryption schemes. This study, based on experiments ran on MNIST and Census Income datasets, shows that neural networks are able to partially recover information that should have been kept secret. Hence, great care should be taken when using the currently available functional encryption schemes to build privacy-preserving classification services. It should be emphasized that this work does not attack the cryptographic security of functional encryption schemes, it rather warns the community against the fact that they should be used with caution for some use cases and that the current state-ofthe-art may lead to some operational weaknesses that could be mitigated in the future once more powerful functional encryption schemes are available.

In 2009, Craig Gentry introduced the first "fully" homomorphic encryption scheme allowing arbitrary circuits to be evaluated on encrypted data. Homomorphic encryption is a very powerful cryptographic primitive, though it has often been viewed by practitioners as too inefficient for practical applications. However, the performance of these encryption schemes has come a long way from that of Gentry's original work: there are now several well-maintained libraries implementing homomorphic encryption schemes and protocols demonstrating impressive performance results, alongside an ongoing standardization effort by the community. In this tutorial we survey the existing homomorphic encryption landscape, providing both a general overview of the state of the art, as well as a deeper dive into several of the existing libraries. We aim to provide a thorough introduction to homomorphic encryption accessible by the broader computer security community. Several of the presenters are core developers of well-known publicly available homomorphic encryption libraries, and organizers of the homomorphic encryption standardization effort \hrefhttp://homomorphicencryption.org/. This tutorial is targeted at application developers, security researchers, privacy engineers, graduate students, and anyone else interested in learning the basics of modern homomorphic encryption.The tutorial is divided into two parts: Part I is accessible by everyone comfortable with basic college-level math; Part II will cover more advanced topics, including descriptions of some of the different homomorphic encryption schemes and libraries, concrete example applications and code samples, and a deeper discussion on implementation challenges. Part II requires the audience to be familiar with modern C++.

International audience

This paper is devoted to the study of the problem of running compression algorithms in the encrypted domain, using a (somewhat) fully homomorphic encryption (FHE) scheme. We do so with a particular focus on conservative compression algorithms. Despite of the encrypted domain Turingcompleteness which comes with the magic of FHE operators, we show that a number of subtleties crop up when it comes to running compression algorithms and, in particular, that guaranteed conservative compression is not possible to achieve in the FHE setting. To illustrate these points, we analyze the most elementary conservative compression algorithm of all, namely Run-Length Encoding (RLE). We first study the way to regularize this algorithm in order to make it (meaningfully) fit within the constraints of a FHE execution. Secondly, we analyze it from the angle of optimizing the resulting structure towards (as much as possible) FHE execution efficiency. The paper is concluded by concrete experimental results obtained using the Fan-Vercauteren cryptosystem as well as the Armadillo FHE compiler. It is also this paper intent to share the concrete return on experience we gained in attempting to run a simple yet practically significant algorithm over FHE.

In this work, we study the practical security of inner-product functional encryption. We left behind the mathematical security proof of the schemes, provided in the literature, and focus on what attackers can use in realistic scenarios without tricking the protocol, and how they can retrieve more than they should be able to. This study is based on the proposed protocol from [1]. We generalize the scenario to an attacker possessing n secret keys. We propose attacks based on machine learning, and experiment them over the MNIST dataset [2].

The use of remote services offered by cloud providers have been popular in the last lustrum. Services allow users to store remote files, or to analyze data for several purposes, like health-care or message analysis. However, when personal data are sent to the Cloud, users may lose privacy on the data-content, and on the other side cloud providers may use those data for their own businesses. In this paper, we present our solution to analyze users health-data directly into the Cloud while preserving users privacy. Our solution makes use of homomorphic encryption to protect users data during the analysis. In particular, we developed a mobile application that offloads users data into the Cloud, and a homomorphic encryption algorithm that processes those data without leaking any information to the Cloud provider. Performed empirical tests show that our HE algorithm is able to evaluate users data in reasonable time proving the feasibility of this emerging way of private-data evaluation.