Privacy-Preserving Near Neighbor Search via Sparse Coding with Ambiguation

TL;DR

This paper introduces a privacy-preserving near neighbor search method using sparse coding with ambiguation, ensuring shared secrecy and fairness across data representations, applicable to various data types and tested on synthetic and real datasets.

Contribution

It presents a novel sparse coding with ambiguation framework for privacy-preserving search, emphasizing fairness and applicability to multiple data forms.

Findings

Effective privacy preservation demonstrated on synthetic and real datasets.

Fairness-aware approach ensures equal probability for neighborhood points.

Applicable to raw, latent, and aggregated data representations.

Abstract

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.