2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIFS-6.1
Paper Title PRIVACY-PRESERVING NEAR NEIGHBOR SEARCH VIA SPARSE CODING WITH AMBIGUATION
Authors Behrooz Razeghi, University of Geneva, Switzerland; Sohrab Ferdowsi, HES-SO Geneva, Switzerland; Dimche Kostadinov, University of Zurich, Switzerland; Flavio P. Clamon, Harvard University, United States; Slava Voloshynovskiy, University of Geneva, United States
SessionIFS-6: Anonymization, Security and Privacy
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
Presentation Poster
Topic Information Forensics and Security: [MMH] Multimedia Content Hash
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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 image databases.