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A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval

Li Weng 1 Laurent Amsaleg 2 Stéphane Marchand-Maillet 1 April Morton 1 
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We propose a privacy protection framework for large-scale content-based information retrieval. It offers two layers of protection. First, robust hash values are used as queries instead of original content or features. Second, the client can choose to omit certain bits in a hash value to further increase the ambiguity for the server. Due to the reduced information, it is computationally difficult for the server to know the client's interest. The server has to return the hash values of all possible candidates to the client. The client performs a search within the candidate list to find the best match. Since only hash values are exchanged between the client and the server, the privacy of both parties is protected.
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Reports (Technical report)
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Contributor : Laurent Amsaleg Connect in order to contact the contributor
Submitted on : Monday, September 1, 2014 - 11:47:10 AM
Last modification on : Wednesday, October 26, 2022 - 8:14:03 AM


  • HAL Id : hal-01059560, version 1


Li Weng, Laurent Amsaleg, Stéphane Marchand-Maillet, April Morton. A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval. [Technical Report] 14.01, 2014. ⟨hal-01059560⟩



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