Privacy-preserving Near Neighbor Search Via Sparse Coding With Ambiguation
Behrooz Razeghi, Sohrab Ferdowsi, Dimche Kostadinov, Flavio. P. Calmon, Slava Voloshynovskiy . ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021 – 0 citations
[Paper]
In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding. The core of the framework relies on sparse coding with ambiguation (SCA) mechanism that introduces the notion of inherent shared secrecy based on the support intersection of sparse codes. This approach is `fairness-aware’, in the sense that any point in the neighborhood has an equiprobable chance to be chosen. Our approach can be applied to raw data, latent representation of autoencoders, and aggregated local descriptors. The proposed method is tested on both synthetic i.i.d data and real large-scale image databases.