Throughput constrained parallelism reduction in cyclo-static dataflow applications

International audienceThis paper deals with semantics-preserving parallelism reduction methods for cyclo-static dataflow applications. Parallelism reduction is the process of equivalent actors fusioning. The principal objectives of parallelism reduction are to decrease the memory footprint of an application and to increase its execution performance. We focus on parallelism reduction methodologies constrained by application throughput. A generic parallelism reduction methodology is introduced. Experimental results are provided for asserting the performance of the proposed method

Stream ciphers: A Practical Solution for Efficient Homomorphic-Ciphertext Compression

International audienceIn typical applications of homomorphic encryption, the first step consists for Alice to encrypt some plaintext m under Bob’s public key pk and to send the ciphertext c = HEpk(m) to some third-party evaluator Charlie. This paper specifically considers that first step, i.e. the problem of transmitting c as efficiently as possible from Alice to Charlie. As previously noted, a form of compression is achieved using hybrid encryption. Given a symmetric encryption scheme E, Alice picks a random key k and sends a much smaller ciphertext c′ = (HEpk(k), Ek(m)) that Charlie decompresses homomorphically into the original c using a decryption circuit CE−1 .In this paper, we revisit that paradigm in light of its concrete implemen- tation constraints; in particular E is chosen to be an additive IV-based stream cipher. We investigate the performances offered in this context by Trivium, which belongs to the eSTREAM portfolio, and we also pro- pose a variant with 128-bit security: Kreyvium. We show that Trivium, whose security has been firmly established for over a decade, and the new variant Kreyvium have an excellent performance

Manticore: Efficient Framework for Scalable Secure Multiparty Computation Protocols

We propose a novel MPC framework, Manticore, in the multiparty setting, with full threshold and semi-honest security model, supporting a combination of real number arithmetic (arithmetic shares), Boolean arithmetic (Boolean shares) and garbled circuits (Yao shares). In contrast to prior work [MZ17, MR18], Manticore never overflows, an important feature for machine learning applications. It achieves this without compromising efficiency or security. Compared to other overflow-free recent techniques such as MP-SPDZ [EGKRS20] that convert arithmetic to Boolean shares, we introduce a novel highly efficient modular lifting/truncation method that stays in the arithmetic domain. We revisit some of the basic MPC operations such as real-valued polynomial evaluation, division, logarithm, exponential and comparison by employing our modular lift in combination with existing efficient conversions between arithmetic, Boolean and Yao shares. Furthermore, we provide a highly efficient and scalable implementation supporting logistic regression models with real-world training data sizes and high numerical precision through PCA and blockwise variants (for memory and runtime optimizations). On a dataset of 50 million rows and 50 columns distributed among two players, it completes in one day with at least 10 decimal digits of precision.Our logistic regression solution placed first at Track 3 of the annual iDASH’2020 Competition. Finally, we mention a novel oblivious sorting algorithm built using Manticore

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby ???tag??? variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation

Ordonnancement pour la gestion de la mémoire et du préchargement dans les architectures multicoeurs embarquées

This PhD thesis is devoted to the study of several combinatorial optimization problems which arise in the field of parallel embedded computing. Optimal memory management and related scheduling problems for dataflow applications executed on massively multi-core processors are studied. Two memory access optimization techniques are considered: data reuse and prefetch. The memory access management is instantiated into three combinatorial optimization problems. In the first problem, a prefetching strategy for dataflow applications is investigated so as to minimize the application execution time. This problem is modeled as a hybrid flow shop under precedence constraints, an \mathcal{NP}\text{-hard} problem. An heuristic resolution algorithm together with two lower bounds are proposed so as to conservatively, though fairly tightly, estimate the distance to the optimality. The second problem is concerned by optimal prefetch management strategies for branching structures (data-controlled tasks). Several objective functions, as well as prefetching techniques, are examined. In all these cases polynomial resolution algorithms are proposed. The third studied problem consists in ordering a set of tasks so as to minimize the number of times the memory data are fetched. In this way the data reuse for a set of tasks is optimized. This problem being \mathcal{NP}\text{-hard} , a result we have established, we have proposed two heuristic algorithms. The optimality gap of the heuristic solutions is estimated using exact solutions. The latter ones are obtained using a branch and bound method we have proposed.Cette thèse est consacrée à l'étude de plusieurs problèmes d'optimisation combinatoire qui se présentent dans le domaine du calcul parallèle embarqué. En particulier, la gestion optimale de la mémoire et des problèmes d'ordonnancement pour les applications flot de données exécutées sur des processeurs massivement multicœurs sont étudiés. Deux techniques d'optimisation d'accès à la mémoire sont considérées : la réutilisation des données et le préchargement. La gestion des accès à la mémoire est déclinée en trois problèmes d'optimisation combinatoire. Dans le premier problème, une stratégie de préchargement pour les applications flot de données est étudiée, de façon à minimiser le temps d'exécution de l'application. Ce problème est modélisé comme un flow shop hybride sous contraintes de précédence, un problème \mathcal{NP}\text{-difficile} . Un algorithme de résolution heuristique avec deux bornes inférieures sont proposés afin de faire une estimation conservatrice, quoique suffisamment précise, de la distance à l'optimum des solutions obtenues. Le deuxième problème traite de l'exécution conditionnelle dépendante des données et de la gestion optimale du préchargement pour les structures de branchement. Quelques fonctions économiques, ainsi que des techniques de préchargement, sont examinées. Dans tous ces cas des algorithmes de résolution polynomiaux sont proposés. Le troisième problème consiste à ordonner un ensemble de tâches de façon à maximiser la réutilisation des données communes. Ce problème étant \mathcal{NP}\text{-difficile} , ce que nous avons établi, nous avons proposé deux algorithmes heuristiques. La distance à l'optimum des solutions est estimée en utilisant des solutions exactes. Ces dernières sont obtenues à l'aide d'une méthode branch-and-bound que nous avons proposée

Armadillo: a compilation chain for privacy preserving applications

In this work we present Armadillo a compilation chain used for com-piling applications written in a high-level language (C++) to work on encrypted data. The back-end of the compilation chain is based on homo-morphic encryption. The tool-chain further automatically handle a huge amount of parallelism so as to mitigate the performance overhead of using homomorphic encryption.

Practical Personalized Genomics in the Encrypted Domain

International audienceIn this paper, we examine and propose a solution for the challenges of sharing of genome sequence data and of data querying on the genome sequence data on a cloud server in per-sonalized medicine scenarios. We develop a privacy-preserving, a secure and efficient solution for personalized medicine. The solution that we propose making use of stream cipher-based homomorphic transciphering in a cloud server, and to show the effectiveness of transciphering solution in the personalized medicine scenario. This paper also provides the comparative analysis of well-known existing homomorphic encryption solutions BGV and FV schemes combined with the FLIP stream cipher to demonstrate the efficiency and privacy of our solution

Scheduling for memory management and prefetch in embedded multi-core architectures

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