Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
This work addresses privacy concerns in identification systems for applications like biometrics or secure databases, though it appears incremental as it builds on existing sparse coding and privacy techniques.
The authors tackled the problem of privacy-preserving identification by proposing a layered sparse coding framework that enables efficient similarity search across distributed servers with varying trust levels, achieving computational and storage efficiency in encoding, decoding, and privacy operations.
We propose a new computationally efficient privacy-preserving identification framework based on layered sparse coding. The key idea of the proposed framework is a sparsifying transform learning with ambiguization, which consists of a trained linear map, a component-wise nonlinearity and a privacy amplification. We introduce a practical identification framework, which consists of two phases: public and private identification. The public untrusted server provides the fast search service based on the sparse privacy protected codebook stored at its side. The private trusted server or the local client application performs the refined accurate similarity search using the results of the public search and the layered sparse codebooks stored at its side. The private search is performed in the decoded domain and also the accuracy of private search is chosen based on the authorization level of the client. The efficiency of the proposed method is in computational complexity of encoding, decoding, "encryption" (ambiguization) and "decryption" (purification) as well as storage complexity of the codebooks.