Abstract :
[en] 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 faces.
Name of the research project :
R-AGR-0686-1 > C11/IS/1204105 : FAVE > 01/01/2012 - 31/12/2014 > OTTERSTEN Björn
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