CASO: Cost-Aware Secure Outsourcing of General Computational Problems
Kai Zhou, Jian Ren

TL;DR
This paper introduces CASO, a cost-aware secure outsourcing scheme for general computational problems that balances security and computational overhead, ensuring data privacy and solution validity in cloud computing.
Contribution
It proposes affine mapping based problem transformation and verification schemes that enhance security while maintaining practical computational overhead.
Findings
CASO effectively balances security and computational costs.
The scheme ensures end-user data privacy during outsourcing.
It provides a verification method for solution correctness.
Abstract
Computation outsourcing is an integral part of cloud computing. It enables end-users to outsource their computational tasks to the cloud and utilize the shared cloud resources in a pay-per-use manner. However, once the tasks are outsourced, the end-users will lose control of their data, which may result in severe security issues especially when the data is sensitive. To address this problem, secure outsourcing mechanisms have been proposed to ensure security of the end-users' outsourced data. In this paper, we investigate outsourcing of general computational problems which constitute the mathematical basics for problems emerged from various fields such as engineering and finance. To be specific, we propose affine mapping based schemes for the problem transformation and outsourcing so that the cloud is unable to learn any key information from the transformed problem. Meanwhile, the…
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Taxonomy
TopicsCryptography and Data Security · Cloud Data Security Solutions · Privacy-Preserving Technologies in Data
