Secure Outsourcing of Large-Scale Convex Optimization Problem in Internet of Things

With the development of cloud computing and the advent of Internet of Things (IoT), outsourcing computation, as an important application of cloud computing, has been widely researched in the field of academic and industry. The convex optimization problem, as a most common mathematical problem, often appears in some machine learning algorithms and smart grid designs. However, the process of solving the convex optimization problem is very complicated and time consuming. For some resource-constrained IoT devices, there are no enough computation resources and storage resources to deal with this problem. In this article, we proposed an efficient and secure outsourcing algorithm for solving the large-scale convex optimization problem with equality constraints in IoT. Our proposed algorithm can not only reduce the computational complexity on the client side, but also protect the client's sensitive data from being disclosed to the dishonest cloud server. In addition, the client can detect the malicious behavior from the cloud server with probability approximately 1. Finally, we give a theoretical analysis about correctness and security, and conduct experiments to show the feasibility of our proposed algorithm.

Automated summary

In this article, an efficient and secure outsourcing algorithm is proposed for solving large-scale convex optimization problems in the Internet of Things (IoT). The algorithm reduces computational complexity on the client side, protects sensitive data, and enables detection of malicious behavior from the cloud server.