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Privacy-Preserving Content-Based Image Retrieval in the Cloud

Storage requirements for visual data has been increasing in recent years, following the emergence of many new services and applications for both personal and corporate use. This has been a key driving factor for the adoption of cloud-based data outsourcing solutions. However, outsourcing data storage to the Cloud also leads to new challenges that must be carefully addressed, specially regarding privacy. In this paper we propose a novel secure framework for outsourced and distributed privacy-preserving storage and retrieval in large image repositories. Our proposal is based on a novel cryptographic scheme, named IES-CBIR, specifically designed for media image data. Our solution enables both encrypted storage and querying using Content Based Image Retrieval (CBIR) while preserving privacy. We have built a prototype of the proposed framework, analyzed its security properties, and experimentally evaluated its performance and precision. Our results show that IES-CBIR allows more efficient operations than existing proposals, both in terms of time and space complexity, while enabling less restrictive use cases and application scenarios.


Description: Available in ARXIV, Cornell University Library. Keywords: Computer Science - Cryptography and Security Bibliographic code: 2014arXiv1411.4862F Cite as: arXiv:1411.4862 [cs.CR]

Date: November, 2014

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