SPN2: Single-sided privacy preserving nearest neighbor and its application to face recognition

We address the privacy concerns that raise when running a nearest neighbor (NN) search on confidential data in a surveillance system composed of a client and a server. The proposed privacy preserving NN search uses Boneh-Goh-Nissim encryption to hide both the query data captured by the client and the database records stored in the server. As opposed to state-of-the-art approaches which rely on a large number of interactions, this encryption enables the client to fully outsource the NN computation to the server; hence, ensuring a single-sided private computation, and resulting in a one-round protocol between the server and the client. We analyze the practical feasibility of this algorithm on a face recognition problem. We formally prove and experimentally show that the resulting system maintains the recognition rate while fully preserving the privacy of both the database and the acquired faces1.

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