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We propose a new computationally efficient privacy-preserving identificationframework based on layered sparse coding. The key idea of the proposedframework is a sparsifying transform learning with ambiguization, whichconsists of a trained linear map, a component-wise nonlinearity and a privacyamplification. We introduce a practical identification framework, whichconsists of two phases: public and private identification. The public untrustedserver provides the fast search service based on the sparse privacy protectedcodebook stored at its side. The private trusted server or the local clientapplication performs the refined accurate similarity search using the resultsof the public search and the layered sparse codebooks stored at its side. Theprivate search is performed in the decoded domain and also the accuracy ofprivate search is chosen based on the authorization level of the client. Theefficiency of the proposed method is in computational complexity of encoding,decoding, "encryption" (ambiguization) and "decryption" (purification) as wellas storage complexity of the codebooks.

Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization