International audience

International audience

Threshold signature schemes enable a group of users to collaboratively produce digital signatures without revealing any individual share. With the current NIST post-quantum standardization underway, the lack of efficient and practical threshold variants of standardized schemes hinders adoption. We introduce the first threshold signature scheme compatible with the ML-DSA standard (Module-Lattice-based Digital Signature Algorithm), supporting up to 6 parties, while retaining efficient signing. Our work uses advanced short secret sharing techniques and optimized rejection sampling to balance communication and correctness in distributed settings. We implement our construction in Go and benchmark it in local, LAN, and WAN deployments. Results show that our threshold ML-DSA is both practical and compatible with real-world applications such as multi-device cryptocurrency wallets, threshold TLS, and Tor's directory authorities.

Subversion-resilient Authenticated key-exchange (AKE) aims to achieve the guarantees of secure AKE even in the presence of an adversary that has tampered with parts of the protocol's implementation. One way to achieve subversion-resilient AKE is the use of Reverse Firewalls (RFs), an untrusted third-party that can restore security. Recent work[17] highlights the challenges of designing RFs for practical secure channel-establishment.

We proposeGoldFinger, a newcompactandfast-to-computebinary 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’ privacyfor freeby providingk-anonymity andl-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$\times 3.55$over one of the most efficient approaches to this problem.