Outsourced image Recovery service from compressed sensing with privacy assurance

A recent data sensing and reconstruction framework well-known for its simplicity of unifying the traditional sampling and compression for data acquisition. To leverage compressed sensing to compress the storage of correlated image datasets. The idea is to store the compressed image samples instead of the whole image, either in compressed or uncompressed format, on storage servers. Their results show that storing compressed samples offers about 50% storage reduction compared to storing the original image in uncompressed format or other data application scenarios where data compression may not be done. But their work does not consider security in mind, which is an indispensable design requirement in OIRS. In fact, compared to that only focuses on storage reduction, our proposed OIRS aims to achieve a much more ambitious goal, which is an outsourced image service platform and takes into consideration of security, efficiency, effectiveness, and complexity from the very beginning of the service flow. Another interesting line of research loosely related to the proposed OIRS is about the security and robustness of compressed sensing based encryption. Those works explore the inherent security strength of linear measurement provided by the process of compressed sensing. The authors have shown that if the sensing matrix is unknown to the adversary, then the attempt to exhaustive searching based original data recovery can be considered as computationally infeasible. This privacy-preserving image recovery service in OIRS that we propose to explore is also akin to the literature of secure computation outsourcing which aims to protects both input and output privacy of the outsourced computations. With the breakthrough on fully homomorphic encryption (FHE). The idea is to represent any computation via a garbled combinational circuit and then evaluate it using encrypted input based on FHE.