Preserving privacy for secure and outsourcing for Linear Programming in cloud computing

TL;DR

This paper presents a privacy-preserving auditing system for cloud data storage and explores secure outsourcing of linear programming computations, combining cryptographic techniques for efficiency and security in cloud environments.

Contribution

It introduces a novel public auditing scheme using homomorphic authentication and random masking, and extends to secure linear programming outsourcing with efficiency and security guarantees.

Findings

Proposed schemes are provably secure.

Achieved high efficiency in multi-user auditing.

Successfully outsourced linear programming securely.

Abstract

Cloud computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. we utilize the public key based homomorphism authenticator and uniquely integrate it with random mask technique to achieve a privacy-preserving public auditing system for cloud data storage security while keeping all above requirements in mind. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously along with investigates secure outsourcing of widely applicable linear programming (LP) computations. In order to achieve practical efficiency, our mechanism design explicitly decomposes the LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.