Efficient and Privacy-Preserving Cloud-Assisted Two-Party Computation Scheme in Heterogeneous Networks

Prevailing smart devices collect individual or industrial sensitive data for collaborative computation to provide convenient service in heterogeneous networks. Nowadays, protecting privacy and security is a significant issue and raises increasing concerns in academia and industry. But diverse smart devices are equipped with unequal resources and some devices with limited resources cannot afford expensive privacy-preserving computation. In this article, we propose a generic efficient and privacy-preserving cloud-assisted two-party computation scheme for smart devices in heterogeneous networks. We adopt the cloud server to assist the collaborative computation and reduce the overhead of smart devices. Besides, we apply preprocessing and online phases to guarantee different devices to operate with a lower burden online. What is more, the work is, to our best knowledge, the first to resist the malicious cloud server and computing parties simultaneously by adopting authenticated masked bits to strengthen the garbled circuit scheme. At the same time, our scheme can guarantee correctness and fairness, as shown in security analysis. The performance comparison result shows that this work is efficient and surpasses the previous best counterpart scheme while maintaining nearly identical computation cost. It outperforms in terms of total communication cost by 49% and total execution time by 32%, even though it takes extra and acceptable cost in the online phase for stronger security against the malicious server.