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In this paper, we consider a privacy preserving encoding framework foridentification applications covering biometrics, physical object security andthe Internet of Things (IoT). The proposed framework is based on a sparsifyingtransform, which consists of a trained linear map, an element-wisenonlinearity, and privacy amplification. The sparsifying transform and privacyamplification are not symmetric for the data owner and data user. Wedemonstrate that the proposed approach is closely related to sparse ternarycodes (STC), a recent information-theoretic concept proposed for fastapproximate nearest neighbor (ANN) search in high dimensional feature spacesthat being machine learning in nature also offers significant benefits incomparison to sparse approximation and binary embedding approaches. Wedemonstrate that the privacy of the database outsourced to a server as well asthe privacy of the data user are preserved at a low computational cost, storageand communication burdens.

Privacy Preserving Identification Using Sparse Approximation with Ambiguization