Paper 2021/1517

HOLMES: A Platform for Detecting Malicious Inputs in Secure Collaborative Computation

Weikeng Chen and Katerina Sotiraki and Ian Chang and Murat Kantarcioglu and Raluca Ada Popa

Abstract

Though maliciously secure multiparty computation (SMPC) ensures confidentiality and integrity of the computation from malicious parties, malicious parties can still provide malformed inputs. As a result, when using SMPC for collaborative computation, input can be manipulated to perform biasing and poisoning attacks. Parties may defend against many of these attacks by performing statistical tests over one another’s input, before the actual computation. We present HOLMES, a platform for expressing and performing statistical tests securely and efficiently. Using HOLMES, parties can perform well-known statistical tests or define new tests. For efficiency, instead of performing such tests naively in SMPC, HOLMES blends together zero-knowledge proofs (ZK) and SMPC protocols, based on the insight that most computation for statistical tests is local to the party who provides the data. High-dimensional tests are critical for detecting malicious inputs but are prohibitively expensive in secure computation. To reduce this cost, HOLMES provides a new secure dimensionality reduction procedure tailored for high-dimensional statistical tests. This new procedure leverages recent development of algebraic pseudorandom functions. Our evaluation shows that, for a variety of statistical tests, HOLMES is 18x to 40x more efficient than naively implementing the statistical tests in a generic SMPC framework.