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Identification with privacy protection based on data hiding

In many problems such as biometrics, multimedia search and retrieval, recommendation systems requiring privacy-preserving similarity computations and identification, some binary features are stored in the public domain or outsourced to the third parties that might raise certain privacy concerns about the original data. To avoid this privacy leak, privacy protection is used. In the most cases, the privacy protection is uniformly applied to all binary features resulting in the data degradation and corresponding loss of performance. To avoid this undesirable effect we propose a new privacy amplification technique that is based on data hiding principles and benefits from side information about bit reliability a.k.a. soft fingerprinting. In this paper, we investigate the identification rate vs. privacy-leak trade-off. The analysis is performed for the cause of perfect match between the side information shared between the encoder and decoder as well as for the case of partial side information.