Adversarially Robust Property-Preserving Hash Functions

Authors Elette Boyle, Rio LaVigne, Vinod Vaikuntanathan

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Elette Boyle
  • IDC Herzliya, Kanfei Nesharim Herzliya, Israel
Rio LaVigne
  • MIT CSAIL, 32 Vassar Street, Cambridge MA, 02139 USA
Vinod Vaikuntanathan
  • MIT CSAIL, 32 Vassar Street, Cambridge MA, 02139 USA

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Elette Boyle, Rio LaVigne, and Vinod Vaikuntanathan. Adversarially Robust Property-Preserving Hash Functions. In 10th Innovations in Theoretical Computer Science Conference (ITCS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 124, pp. 16:1-16:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Property-preserving hashing is a method of compressing a large input x into a short hash h(x) in such a way that given h(x) and h(y), one can compute a property P(x, y) of the original inputs. The idea of property-preserving hash functions underlies sketching, compressed sensing and locality-sensitive hashing. Property-preserving hash functions are usually probabilistic: they use the random choice of a hash function from a family to achieve compression, and as a consequence, err on some inputs. Traditionally, the notion of correctness for these hash functions requires that for every two inputs x and y, the probability that h(x) and h(y) mislead us into a wrong prediction of P(x, y) is negligible. As observed in many recent works (incl. Mironov, Naor and Segev, STOC 2008; Hardt and Woodruff, STOC 2013; Naor and Yogev, CRYPTO 2015), such a correctness guarantee assumes that the adversary (who produces the offending inputs) has no information about the hash function, and is too weak in many scenarios. We initiate the study of adversarial robustness for property-preserving hash functions, provide definitions, derive broad lower bounds due to a simple connection with communication complexity, and show the necessity of computational assumptions to construct such functions. Our main positive results are two candidate constructions of property-preserving hash functions (achieving different parameters) for the (promise) gap-Hamming property which checks if x and y are "too far" or "too close". Our first construction relies on generic collision-resistant hash functions, and our second on a variant of the syndrome decoding assumption on low-density parity check codes.

Subject Classification

ACM Subject Classification
  • Theory of computation → Cryptographic primitives
  • Hash function
  • compression
  • property-preserving
  • one-way communication


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