Privacy-Assured Outsourcing of Compressed Sensing Reconstruction Service in Cloud
This addresses privacy and security concerns for end-users in multimedia big data applications, though it is incremental as it builds on existing cloud outsourcing methods.
The paper tackles the problem of securely outsourcing compressed sensing reconstruction to the cloud while preserving privacy, proposing a scheme that restricts malicious access, verifies data integrity, and resists various attacks like brute-force and ciphertext-only attacks.
Compressed sensing (CS), breaking the constriction of Shannon-Nyquist sampling theorem, is a very promising data acquisition technique in the era of multimedia big data. However, the high complexity of CS reconstruction algorithm is a big trouble for endusers who are hardly provided with great computing power. The combination of CS and cloud has the potential of freeing endusers from the resource constraint by cleverly transforming computational workload from the local cilent to the cloud platform. As a result, the low-complexity encoding virtue of CS is fully leveraged in the resource-constrained sensing devices but its highcomplexity decoding problem is effectively addressed in cloud. It seems to be perfect but privacy and security concerns are ignored. In this paper, a secure outsourcing scheme for CS reconstruction service is proposed. Experimental results and security analyses demonstrate that the proposed scheme can restrict malicious access, verify the integrity of the recovered data, and resist brute-force attack, ciphertext-only attack, and plaintext attack.