Privacy preserving identification using sparse approximation with ambiguization

Abstract

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.

Reconstruction of Privacy-Sensitive Data from Protected Templates

“…We believe that these factors should be covered in future research. In particular, the lack of reliably image priors can be addressed by the generative model in the formulation (8) under the linear ternarization approximation and compared with the solution based on (9). An additional research problem to be addressed is an adversarial training (9), when the various ambiguizations u a should replace the clean version u similarly to the denoising auto-encoder training [22].…”

“…We do not pretend to be exhaustive in our overview and refer interesting readers to [8]. Recently, a concept of the STCA was proposed that combines and extends the encoding and randomization principles from the informationtheoretic perspectives [9][10][11]. The STCA ensures the protection of both templates and queries in authentication and identification systems against the adversarial reconstruction and clustering [9,10].…”

Single-Component Privacy Guarantees in Helper Data Systems and Sparse Coding with Ambiguation

“…G. Sparse Coding with Amibiguation (SCA) [9], [10] Given a sparse ternary vector v = ϕ(x) ∈ {−1, 0, 1} L , the ambiguation mechanism A turns S n randomly chosen zero components of v into a (random) ±1. The resulting ternary vector u = A(v) is stored as enrolment data (see Fig.…”

Privacy Preserving Group Membership Verification and Identification

“…At their core, these approaches use for example sketching or quantization techniques that prohibit reconstructing identities with sufficient precision [2,3]. Other techniques embed real vectors into another representational space with the purpose of security and privacy [12,11].…”