International Association for Cryptologic Research

International Association
for Cryptologic Research


Victor I. Kolobov


Programmable Distributed Point Functions 📺
Victor I. Kolobov Elette Boyle Niv Gilboa Yuval Ishai
A distributed point function (DPF) is a cryptographic primitive that enables compressed additive sharing of a secret unit vector across two or more parties. Despite growing ubiquity within applications and notable research efforts, the best 2-party DPF construction to date remains the tree-based construction from (Boyle et al, CCS’16), with no significantly new approaches since. We present a new framework for 2-party DPF construction, which applies in the setting of feasible (polynomial-size) domains. This captures in particular all DPF applications in which the keys are expanded to the full domain. Our approach is motivated by a strengthened notion we put forth, of programmable DPF (PDPF): in which a short, input-independent “offline” key can be reused for sharing many point functions. – PDPF from OWF. We construct a PDPF for feasible domains from the minimal assumption that one-way functions exist, where the second “online” key size is polylogarithmic in the domain size N. Our approach offers multiple new efficiency features and applications: – Privately puncturable PRFs. Our PDPF gives the first OWF-based privately puncturable PRFs (for feasible domains) with sublinear keys. – O(1)-round distributed DPF Gen. We obtain a (standard) DPF with polylog-size keys that admits an analog of Doerner-shelat (CCS’17) distributed key generation, requiring only O(1) rounds (versus log N). – PCG with 1 short key. Compressing useful correlations for secure computation, where one key size is of minimal size. This provides up to exponential communication savings in some application scenarios.
On Computational Shortcuts for Information-Theoretic PIR 📺
Information-theoretic {\em private information retrieval} (PIR) schemes have attractive concrete efficiency features. However, in the standard PIR model, the computational complexity of the servers must scale linearly with the database size. We study the possibility of bypassing this limitation in the case where the database is a truth table of a ``simple'' function, such as a union of (multi-dimensional) intervals or convex shapes, a decision tree, or a DNF formula. This question is motivated by the goal of obtaining lightweight {\em homomorphic secret sharing} (HSS) schemes and secure multiparty computation (MPC) protocols for the corresponding families. We obtain both positive and negative results. For ``first-generation'' PIR schemes based on Reed-Muller codes, we obtain computational shortcuts for the above function families, with the exception of DNF formulas for which we show a (conditional) hardness results. For ``third-generation'' PIR schemes based on matching vectors, we obtain stronger hardness results that apply to all of the above families. Our positive results yield new information-theoretic HSS schemes and MPC protocols with attractive efficiency features for simple but useful function families. Our negative results establish new connections between information-theoretic cryptography and fine-grained complexity.