Flood and Submerse: Distributed Key Generation and Robust Threshold Signature from Lattices

We propose a new framework based on random submersions — that is projection over a random subspace blinded by a small Gaussian noise — for constructing verifiable short secret sharing and showcase it to construct efficient threshold lattice-based signatures in the hash-and-sign paradigm, when based on noise flooding. This is, to our knowledge, the first hash-and-sign lattice-based threshold signature. Our threshold signature enjoys the very desirable property of robustness, including at key generation. In practice, we are able to construct a robust hash-and-sign threshold signature for threshold and provide a typical parameter set for threshold T = 16 and signature size 13kB. Our constructions are provably secure under standard MLWE assumption in the ROM and only require basic primitives as building blocks. In particular, we do not rely on FHE-type schemes

GoldFinger: Fast & Approximate Jaccard for Efficient KNN Graph Constructions

International audienceWe propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard's index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and show that GoldFinger can drastically accelerate a large range of existing KNN algorithms with little to no overhead. As a side effect, we also show that the compact representation of the data protects users' privacy for free by providing k-anonymity and l-diversity. Our extensive evaluation of the resulting approach on several realistic datasets shows that our approach reduces computation times by up to 78.9% compared to raw data while only incurring a negligible to moderate loss in terms of KNN quality. We also show that GoldFinger can be applied to KNN queries (a widely-used search technique) and delivers speedups of up to ×3.55 over one of the most efficient approaches to this problem