  In this paper, we consider a privacy preserving encoding framework for
identification applications covering biometrics, physical object security and
the Internet of Things (IoT). The proposed framework is based on a sparsifying
transform, which consists of a trained linear map, an element-wise
nonlinearity, and privacy amplification. The sparsifying transform and privacy
amplification are not symmetric for the data owner and data user. We
demonstrate that the proposed approach is closely related to sparse ternary
codes (STC), a recent information-theoretic concept proposed for fast
approximate nearest neighbor (ANN) search in high dimensional feature spaces
that being machine learning in nature also offers significant benefits in
comparison to sparse approximation and binary embedding approaches. We
demonstrate that the privacy of the database outsourced to a server as well as
the privacy of the data user are preserved at a low computational cost, storage
and communication burdens.
