Secure and Private Coding for Edge Computing Against Cooperative Attack with Low Communication Cost and Computational Load

Edge computing is an efficient computing paradigm, which can utilize computing devices at the edge of network to provide real-time proximity service. Since edge devices lack centralized management, they are more vulnerable to being attacked. Therefore, the issues of data security and user privacy in edge computing are particularly important. A large number of existing literature focus on the data security and user privacy with independent attackers. However, cooperative attacks, in which multiple attackers can collaborate to obtain the data content and user privacy, have not been fully investigated. In particular, we take the matrix-vector multiplication which is a basic component of most machine learning algorithms as the basic task. Therefore, in this paper, we focus on theSecure and Privacy Matrix-vector Multiplication(SPMM) issue for edge computing against cooperative attack and design a general coded computation scheme to achieve lowest system resource consumption,i.e.communication cost and computational load. Specifically, we propose two coding schemes:Secure and Private Coding with lower communication Cost(SPCC) andSecure and Private Coding with lower computational Load(SPCL). We also conduct solid theoretical analyses and extensive experiments to demonstrate that both two proposed coding schemes can achieve lower communication cost and computational load than existing work. Finally, we perform extensive analyses to the superiority of the proposed schemes.