Efficient Secure Pattern Matching with Malicious Adversaries

Abstract

In a secure pattern matching scheme, a client learns only the locations where his private pattern matches a server's private text, while server learns nothing. In this paper, we propose a secure pattern matching protocol for the semi-honest setting which is then enhanced to guarantee full simulation-based security in the presence of malicious parties. The proposed protocol supports exact pattern matching, approximate pattern matching and pattern matching with wildcards. It is analytically shown that the proposed protocol is considerably more efficient in the approximate matching with at most k permitted mismatches while it has the same speed in the exact matching case comparing with the recent work in the literature. The achievements are also experimentally evaluated on a case of secure Desoxyribo-Nucleic Acid (DNA) search over the NCBI dataset of the United States national library of medicine. The results show efficiency of the proposed protocol and particularly confirm low computation overhead for the client.