Secure parameterized pattern matching

Abstract

Parameterized pattern matching (PPM) is the problem of matching between two given parameterized strings over two constant and parameter alphabets. PPM has special applications in software maintenance, information retrieval, computational biology, and so on. In some applications of PPM, preserving the privacy of the involved parties is essential. For example, a researcher holding an amino acid pattern needs to receive the parameterized matched locations of his/her input with the patterns in a biological database while the database owner has to obtain no information about the matching results and the pattern. In this paper, we define this problem as secure PPM (SPPM), present a scheme to resolve it in the semi-honest and malicious adversarial models, and prove the security of the proposed scheme in the universal composability (UC) framework. The proposed scheme supports wildcard and approximate PPM, too. We evaluate the security and performance of the proposed scheme. The proposed scheme is experimentally evaluated on a case of secure ribonucleic acid (RNA) search over the RNAcentral dataset. Implementation results show that the proposed scheme is secure and efficient for preserving privacy in contexts where PPM is applicable.