Sourav Das (University of Illinois at Urbana-Champaign), Zhuolun Xiang (Aptos), Ling Ren (University of Illinois at Urbana-Champaign)

The $q$-Strong Diffie-Hellman~($q$-SDH) parameters are foundational to efficient constructions of many cryptographic primitives such as zero-knowledge succinct non-interactive argument of knowledge, polynomial/vector commitments, verifiable secret sharing, and randomness beacon. The only existing method to generate these parameters securely is highly sequential, requires strong network synchrony assumptions, and has very high communication and computation cost. For example, to generate parameters for any given $q$, each party incurs a communication cost of $Omega(nq)$ and requires $Omega(n)$ rounds. Here $n$ is the number of parties in the secure multiparty computation protocol. Since $q$ is typically large, i.e., on the order of billions, the cost is highly prohibitive.

In this paper, we present a distributed protocol to generate $q$-SDH parameters in an asynchronous network. In a network of $n$ parties, our protocol tolerates up to one-third of malicious parties. Each party incurs a communication cost of $O(q + n^2log q)$ and the protocol finishes in $O(log q + log n)$ expected rounds. We provide a rigorous security analysis of our protocol. We implement our protocol and evaluate it with up to 128 geographically distributed parties. Our evaluation illustrates that our protocol is highly scalable and results in a 2-6$times$ better runtime and 4-13$times$ better per-party bandwidth usage compared to the state-of-the-art synchronous protocol for generating $q$-SDH parameters.

View More Papers

CP-IoT: A Cross-Platform Monitoring System for Smart Home

Hai Lin (Tsinghua University), Chenglong Li (Tsinghua University), Jiahai Yang (Tsinghua University), Zhiliang Wang (Tsinghua University), Linna Fan (National University of Defense Technology), Chenxin Duan (Tsinghua University)

Read More

VPN Awareness and Misconceptions: A Comparative Study in Canadian...

Lachlan Moore, Tatsuya Mori (Waseda University, NICT)

Read More

ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

Read More

TEE-SHirT: Scalable Leakage-Free Cache Hierarchies for TEEs

Kerem Arikan (Binghamton University), Abraham Farrell (Binghamton University), Williams Zhang Cen (Binghamton University), Jack McMahon (Binghamton University), Barry Williams (Binghamton University), Yu David Liu (Binghamton University), Nael Abu-Ghazaleh (University of California, Riverside), Dmitry Ponomarev (Binghamton University)

Read More