Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories - 2017


Storage needs for visual knowledge are increasing in recent years, following the emergence of many highly interactive multimedia services and applications for mobile devices in each personal and corporate scenarios. This has been a key driving factor for the adoption of cloud-based mostly knowledge outsourcing solutions. However, outsourcing data storage to the Cloud conjointly results in new security challenges that must be fastidiously addressed, especially regarding privacy. In this paper we have a tendency to propose a secure framework for outsourced privacy-preserving storage and retrieval in giant shared image repositories. Our proposal relies on IES-CBIR, a unique Image Encryption Theme that exhibits Content-Based Image Retrieval properties. The framework enables each encrypted storage and looking using Content-Based mostly Image Retrieval queries while preserving privacy against honest-but-curious cloud administrators. We have a tendency to have built a prototype of the proposed framework, formally analyzed and proven its security properties, and experimentally evaluated its performance and retrieval precision. Our results show that IES-CBIR is provably secure, permits more economical operations than existing proposals, each in terms of time and house complexity, and paves the means for brand spanking new practical application scenarios.

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