Privacy-Assured Outsourcing of Image Reconstruction Service in Cloud

Abstract

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.

System perspectives for mobile platform design in m-Health

“…In the Smart-Health-Systems Lab, we investigated several data security designs for wearable sensor units. 11,12 Our goal is to keep the measurement data secure on the sensor unit, in the data transmission, and on the Cloud. One of the designs is a chip implementation that can process AES (Advanced Encryption Standard) efficiently using CMOS-based circuits.…”

Efficient secure outsourcing of large-scale linear systems of equations

“…3) Main Iteration: Exploring the CGM iteration, i.e., equations (16)-(20), we notice that equations (16) and (17) need matrix-vector multiplications involving the coefficient matrix A ′ , while the rest of the equations only require vectorvector multiplications. We exploit these equations to design an efficient collaborative computation between the CC and the CS, where the CS helps compute (16) and (17), and the CC carries out the rest of the equations by itself.…”

“…Although the scheme is very attractive and offers theoretical privacy guarantees, FHE itself is a computationally intensive operation, and large-scale computations based on FHE ciphertexts are very expensive, even for the cloud. Wang et al [15] [16] propose methods to privately outsource a linear programming problem. A client may employ these methods to find the solution to an LSE by requesting the cloud to solve a special linear program.…”

RAICB: Robust Authentication Immunity Cloud Bucket for Secure Cloud Storage System

“…It can recognize flaws in software apparatus listening on the network. The investigation towards privacy issues was carried out by Wang et al [15] by introducing a privacy protection for data contents in cloud. It is exploitation of the various domains and at begins only it is targeted to consider the design complexity, security and efficiency.…”