Publications

Concretely-Efficient Multi-Key Homomorphic Secret Sharing and Applications

Published in IEEE S&P, 2026

We construct and implement the first, concretely-efficient Multi-Key Homomorphic Secret Sharing (MKHSS) scheme. A powerful application of MKHSS is to realize attribute-based non-interactive key exchange (ANIKE), which generalizes password-authenticated key exchange (PAKE) to arbitrary attribute policies. ANIKE is currently only known from MKHSS and in this paper we show the first practical instantiation of ANIKE for two concrete predicate types with applications to geolocation-based key exchange and fuzzy PAKE.

Passive SSH Key Compromise via Lattices

Published in ACM CCS, 2023

We demonstrate that a passive network attacker can opportunistically obtain private RSA host keys from an SSH server that experiences a naturally arising fault during signature computation. In prior work, this was not believed to be possible for the SSH protocol because the signature included information like the shared Diffie-Hellman secret that would not be available to a passive network observer. We show that for the signature parameters commonly in use for SSH, there is an efficient lattice attack to recover the private key in case of a signature fault. We provide a security analysis of the SSH, IKEv1, and IKEv2 protocols in this scenario, and use our attack to discover hundreds of compromised keys in the wild from several independently vulnerable implementations.

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Critique of: “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team from UC San Diego

Published in IEEE TPDS, 2022

Bayesian networks (BNs) have become popular in recent years to describe natural phenomena in situations where causal linkages are important to understand. In order to get around the inherent non-tractability of learning BNs, Srivastava et al. propose a markov blanket discovery-based approach to learning in their paper titled “A Parallel Framework for Constraint-based Bayesian Network Learning via Markov Blanket Discovery.” We are able to reproduce both the strong and weak scaling experiments from the paper up to 128 cores, and verify communication cost scaling for all three algorithms in the paper. We also introduce methodological improvements to weak scaling that show the paper’s findings are unique to the methodology and not the datasets used. Slight variations in performance were observed due to differences in datasets, core count, and job scheduling.

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