Special Issue Article Open Access

Privacy-Assured OIRS Service with Performance Speedup in Cloud

Abstract

At the moment wide Ranging image data sets are being rapidly generated. Along with such data explosion is the fast-growing vogue to outsource the image management systems to the cloud for its lavish computing resources and benefits.However,how to protect the sensitive data while enabling outsourced image services, becomes a major concern. To address these challenges, we propose outsourced image recovery service (OIRS), a novel outsourced image recovery service architecture, which deeds different domain technologies and takes security, efficiency, and design complexity into consideration from the very beginning of the service flow. Specifically, we choose to design OIRS under the compressed sensing framework, which is known for its simplicity of unifying the traditional sampling and compression for image attainment. Data owners only need to outsource compressed image samples to cloud for diminish storage overhead. In addition, in OIRS, data users can hitch the cloud to securely reconstruct images without enlightening information from either the compressed image samples or the underlying image content. We start with the OIRS design for sparse data, which is the typical application scenario for compressed sensing, and then show its natural extension to the general data for meaningful tradeoffs between efficiency and accuracy. We thoroughly analyze the privacy-protection of OIRS and conduct far reaching experiments to demonstrate the system effectiveness and efficiency. For completeness, we also discuss the expected performance speedup of OIRS through hardware built-in system design.

P.ARUNPRIYA,S.RINESH

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