Harry W. H. Wong (The Chinese University of Hong Kong), Jack P. K. Ma (The Chinese University of Hong Kong), Sherman S. M. Chow (The Chinese University of Hong Kong)

Threshold signatures, notably ECDSA, are fundamental for securing decentralized applications. Their non-linear structure poses challenges in distributed signing, often tackled by pairwise multiplicative-to-additive share conversion, leading to O(n) communication and O(n2) verification costs for each of n signers. Moreover, most schemes lack robustness, necessitating a complete restart upon fault. A pioneering work by Wong et al. (NDSS '23) still requires rolling back to the preceding round to resume signing after another round to convince all other signers.

We revisit secure multiparty computation from threshold linearly homomorphic encryption (LHE). Realizing its public verifiability and fault recovery, we encompass two technical contributions to Castagnos–Laguillaumie LHE (CT-RSA '15): a 2-round robust distributed key generation (DKG) protocol in the dishonest majority setting and an accompanying zero-knowledge proof allowing extraction in an unknown-order group. We extend the DKG with dual-code-based verification (ACNS '17), upgrading its O(tn2)-cost private verifiability to an O(n2) public one.

Built on our DKG, we present the first threshold ECDSA protocol with O(1) communication and O(n) verification per-party costs while matching the lowest round complexity of nonrobust schemes (CCS '20). Empirically, we halve the computation and communication costs of the signing phase compared to state-of-the-art robust threshold ECDSA (NDSS '23). We also illustrate the versatility of our techniques with an improved threshold extension (IEEE S&P '23) of BBS+ signatures (IEEE Syst. J. '13).

View More Papers

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

Make your IoT environments robust against adversarial machine learning...

Hamed Haddadpajouh (University of Guelph), Ali Dehghantanha (University of Guelph)

Read More

Work-in-Progress: Manifest V3 Unveiled: Navigating the New Era of...

Nikolaos Pantelaios and Alexandros Kapravelos (North Carolina State University)

Read More

Strengthening Privacy in Robust Federated Learning through Secure Aggregation

Tianyue Chu, Devriş İşler (IMDEA Networks Institute & Universidad Carlos III de Madrid), Nikolaos Laoutaris (IMDEA Networks Institute)

Read More