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.

We present a novel protocol XORBoost for both training gradient boosted tree models and for using these models for inference in the multiparty computation (MPC) setting. Our protocol supports training for generically split datasets (vertical and horizontal splitting, or combination of those) while keeping all the information about features, thresholds, and evaluation paths private; only tree depth and the number of the binary trees are public parameters of the model. By using novel optimization techniques that reduce the number of oblivious permutation evaluations as well as sorting operations, we further speedup the algorithm. The protocol is agnostic to the underlying MPC framework or implementation.

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++.

To solve the approximate nearest neighbor search problem (NNS) on the sphere, we propose a method using locality-sensitive filters (LSF), with the property that nearby vectors have a higher probability of surviving the same filter than vectors which are far apart. We instantiate the filters using spherical caps of height 1 – α, where a vector survives a filter if it is contained in the corresponding spherical cap, and where ideally each filter has an independent, uniformly random direction. For small α, these filters are very similar to the spherical locality-sensitive hash (LSH) family previously studied by Andoni et al. For larger α bounded away from 0, these filters potentially achieve a superior performance, provided we have access to an efficient oracle for finding relevant filters. Whereas existing LSH schemes are limited by a performance parameter of ρ ≥ 1/(2c2 – 1) to solve approximate NNS with approximation factor c, with spherical LSF we potentially achieve smaller asymptotic values of ρ, depending on the density of the data set. For sparse data sets where the dimension is super-logarithmic in the size of the data set, we asymptotically obtain ρ = 1/(2c2 – 1), while for a logarithmic dimensionality with density constant κ we obtain asymptotics of ρ ∼ 1/(4κc2). To instantiate the filters and prove the existence of an efficient decoding oracle, we replace the independent filters by filters taken from certain structured random product codes. We show that the additional structure in these concatenation codes allows us to decode efficiently using techniques similar to lattice enumeration, and we can find the relevant filters with low overhead, while at the same time not significantly changing the collision probabilities of the filters. We finally apply spherical LSF to sieving algorithms for solving the shortest vector problem (SVP) on lattices, and show that this leads to a heuristic time complexity for solving SVP in dimension n of (3/2)n/2+o(n) ≈ 20.292n+o(n). This asymptotically improves upon the previous best algorithms for solving SVP which use spherical LSH and cross-polytope LSH and run in time 20.298n+o(n). Experiments with the GaussSieve validate the claimed speedup and show that this method may be practical as well, as the polynomial overhead is small.

The celebrated Lenstra-Lenstra-Lovász lattice basis reduction algorithm (LLL) can naturally be viewed as an algorithmic version of Hermite's inequality on Hermite's constant. We present a polynomial-time blockwise reduction algorithm based on duality which can similarly be viewed as an algorithmic version of Mordell's inequality on Hermite's constant. This achieves a better and more natural approximation factor for the shortest vector problem than Schnorr's algorithm and its transference variant by Gama, Howgrave-Graham, Koy and Nguyen. Furthermore, we show that this approximation factor is essentially tight in the worst case.

Summary form only given. Within the trend of object-based distributed computing, we present the design and implementation of a numerical simulation for electromagnetic waves propagation. A sequential Java design and implementation is first presented. Further, a distributed and parallel version is derived from the first, using an active object pattern. In addition, benchmarks are presented on this nonembarrassingly parallel application. A first contribution resides in the sequential object-oriented design that proved to be very modular and extensible; the classes and abstractions are designed to allow both element and volume type methods, furthermore, valid on structured, unstructured, or hybrid meshes. Compared to a Fortran version, the performance of this highly modular version proved to be in the same range. It is also shown how smoothly the sequential version can be distributed, keeping the same structuring and object abstractions, allowing to deal with larger data size. Finally, benchmarks on up to 64 processors compare the performances with respect to sequential and parallel versions, putting that in perspective with a comparable Fortran version.