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Privacy-Preserving Content-Based Image Retrieval in the Cloud
Abstract:

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.


URL: http://arxiv.org/abs/1411.4862

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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