Efficient Generalized Selective Private Function Evaluation with Applications in Biometric Authentication
In a selective private function evaluation (SPFE) protocol, the client privately computes some predefined function on his own input and on m out of server’s n database elements. We propose two new generalized SPFE protocols that are based on the new cryptocomputing protocol by Ishai and Paskin and an efficient CPIR. The first protocol works only for constant values of m, but has 2 messages, and is most efficient when m= 1. The second SPFE protocol works for any m, has 4 messages, and is efficient for a large class of functionalities. We then propose an efficient protocol for private similarity test, where one can compute how similar client’s input is to a specific element in server’s database, without revealing any information to the server. The latter protocol has applications in biometric authentication.
KeywordsBDD biometric authentication CPIR cryptocomputing selective private function evaluation
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