Privacy-preserving algorithm for outsourcing matrix multiplication based on Strassen’s algorithm

Abstract

Matrix Multiplication is a fundamental mathematical tool and basic problem with various applications in many domains such as data mining and data analyzing. Most of the time, limited-resource clients cannot afford the cost of heavy computations and needs to outsource the information and computations to a powerful and efficient cloud system. In this data exchanging, security and privacy concerning become a significant issue due to the value of some sensitive data such as private personal information or industrial data. The untrusted cloud servers beside malicious adversaries are always a kind of hazard for the clients and increase the risk of information leakage in data outsourcing. There exist many secure algorithms with creative data masking methods which are already usable in the industry, but due to numerous system and treat models, and the importance of the level of the security, new algorithms always emerge to improve the existing schemes. In this thesis, we propose four privacy-preserving algorithms based on Strassen’s scheme to provide the desirable result of matrix multiplication for the clients who are not able to process the regular matrix multiplication algorithms because of lake of powerful resources.

Date	4 Feb 2020
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Jean-Marc Robert (Supervisor)