International Association for Cryptologic Research

International Association
for Cryptologic Research


Yiping Ma


SPRINT: High-Throughput Robust Distributed Schnorr Signatures
We describe robust high-throughput threshold protocols for generating Schnorr signatures in an asynchronous setting with potentially hundreds of parties. The protocols run a single message-independent interactive ephemeral randomness generation procedure (i.e., DKG) followed by \emph{non-interactive} signature generation for multiple messages, at a communication cost similar to one execution of a synchronous non-robust protocol in prior work (e.g., Gennaro et al.) and with a large number of parties (ranging from few tens to hundreds and more). Our protocols extend seamlessly to the dynamic/proactive setting where each run of the protocol uses a new committee with refreshed shares of the secret key; in particular, they support large committees periodically sampled from among the overall population of parties and the required secret state is transferred to the selected parties. The protocols work over a broadcast channel and are robust (provide guaranteed output delivery) even over asynchronous networks. The combination of these features makes our protocols a good match for implementing a signature service over a public blockchain with many validators, where guaranteed output delivery is an absolute must. In that setting, there is a system-wide public key, where the corresponding secret signature key is distributed among the validators. Clients can submit messages (under suitable controls, e.g. smart contracts), and authorized messages are signed relative to the global public key. Asymptotically, when running with committees of $n$ parties, our protocols can generate $\Omega(n^2)$ signatures per run, while providing resilience against $\Omega(n)$ corrupted nodes and broadcasting only $O(n^2)$ group elements and scalars (hence $O(1)$ elements per signature). We prove the security of our protocols via a reduction to the hardness of the discrete logarithm problem in the random oracle model.
Compressing Unit-Vector Correlations via Sparse Pseudorandom Generators
A unit-vector (UV) correlation is an additive secret-sharing of a vector of length B that contains 1 in a secret random position and 0's elsewhere. UV correlations are a useful resource for many cryptographic applications, including low-communication secure multiparty computation and multi-server private information retrieval. However, current practical methods for securely generating UV correlations involve a significant communication cost per instance, and become even more expensive when requiring security against malicious parties. In this work, we present a new approach for constructing a pseudorandom correlation generator (PCG) for securely generating n independent instances of UV correlations of any polynomial length B. Such a PCG compresses the n UV instances into correlated seeds whose length is sublinear in the description size n log B. Our new PCGs apply in both the honest-majority and dishonest-majority settings, and are based on a variety of assumptions. In particular, in the honest-majority case they only require "unstructured" assumptions. Our PCGs give rise to secure end-to-end protocols for generating n instances of UV correlations with o(n) bits of communication. This applies even to an authenticated variant of UV correlations, which is useful for security against malicious parties. Unlike previous theoretical solutions, some instances of our PCGs offer good concrete efficiency. Our technical approach is based on combining a low-degree sparse pseudorandom generator, mapping a sparse seed to a pseudorandom sparse output, with homomorphic secret sharing for low-degree polynomials. We then reduce such sparse PRGs to local PRGs over large alphabets, and explore old and new approaches for maximizing the stretch of such PRGs while minimizing their locality. Finally, towards further compressing the PCG seeds, we present a new PRG-based construction of a multiparty distributed point function (DPF), whose outputs are degree-1 Shamir-shares of a secret point function. This result is independently motivated by other DPF applications.