48,203 research outputs found
Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
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.Comment: EUSIPCO 201
INSTRUMENTATION-BASED MUSIC SIMILARITY USING SPARSE REPRESENTATIONS
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Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
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