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These keywords were added by machine and not by the authors. We show that new techniques and optimizations employed in this work allow us to achieve particularly efficient protocols suitable for large data sets and obtain notable performance gain compared to the state-of-the-art prior work. We also design and implement a secure protocol for fingerprint identification based on FingerCodes with a substantial improvement in the performance compared to existing solutions. To address the problem, in this work we develop and implement the first privacy-preserving identification protocol for iris codes. This gives rise to the need to develop secure computation techniques over biometric data where no information is revealed to the parties except the outcome of the comparison or search.
Due to privacy and liability considerations, neither party is willing to share its data. Biometric matching is often performed by two mutually distrustful parties, one of which holds one biometric image while the other owns a possibly large biometric collection. Recent advances in biometric recognition and the increasing use of biometric data prompt significant privacy challenges associated with the possible misuse, loss, or theft of biometric data.
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