FTP: An Approximate Fast Privacy-Preserving Equality Test Protocol for Authentication in Internet of Things

Abstract

Privacy-preserving string equality test is a fundamental operation of many algorithms, including privacy-preserving authentication in Internet of Things (IoT). Existing secure equality test schemes can theoretically achieve string equality comparison and preserve the private strings. However, they suffer from heavy computation and communication cost, especially while the strings are of hundreds of bits or longer, which is not suitable for IoT applications. In this paper, we propose an approximate Fast privacy-preserving equality Test Protocol (FTP), which can securely complete string equality test and achieve high running efficiency at the cost of little accuracy loss. We strictly analyze the accuracy of our proposed scheme and formally prove its security. Additionally, we leverage extensive simulation experiments to evaluate the running cost, which confirms our high efficiency; for instance, our proposed FTP can securely compare two 256-bit strings within 0.7 seconds on ordinary laptops.

PF‐ITS: Intelligent traffic service recommendation based on DeepAFM model

“…Meanwhile, big data, IoT, AI, and other technologies have gradually penetrated into the ITS, 15,16 and they play an increasingly significant role in ITS applications. 17 Ye et al 18 proposed time series models to exert traffic prediction based on data collected from IC payment devices, in which the ARMA model with quadratic trend performs effective passenger flow prediction with high accuracy. 19 With data from client nodes in a wireless sensor network, Thakur et al 20 studied IoT-based solutions in transport environment, which helps motorists obtain prior contextual guidance, so as to reduce congestion and avoid potential hazards.…”

“…The smart systems in urban traffic management, such as automatic driving, data sharing and mobility as a service, become hot research directions to boost the development of ITS. Meanwhile, big data, IoT, AI, and other technologies have gradually penetrated into the ITS, 15,16 and they play an increasingly significant role in ITS applications 17 . Ye et al 18 proposed time series models to exert traffic prediction based on data collected from IC payment devices, in which the ARMA model with quadratic trend performs effective passenger flow prediction with high accuracy 19 .…”