A Tensor-Based Optimization Model for Secure Sustainable Cyber-Physical-Social Big Data Computations

Secure cyber-physical-social big data computations are being increasingly used to protect the users' data security in cyber-physical-social systems (CPSS). Despite the increasing popularity, how to process the tasks of the secure cyber-physical-social big data computations, while taking care of the energy consumption and meeting the users' requirements, remains challenging. To address the problem, in this work, we propose a novel tensor-based optimization model for the secure sustainable cyber-physical-social big data computations. The proposed model is a general and fine-grained model, which can jointly optimize the execution time, energy consumption, reliability, and quality of experience, and can comprehensively take into account step, task, time slot, type, node, core, cryptosystem, and security level. To our knowledge, this is the first study to holistically optimize the tasks in the secure cyber-physical-social big data computations. To illustrate the proposed model, the case study of the secure high-order Lanczos in cloud-assisted CPSS is presented. Finally, the proposed model is empirically evaluated by using multi-objective optimization, and the extensive results demonstrate that from the users' perspective our proposed tensor-based optimization model is preferable for the secure sustainable cyber-physical-social big data computations.