A Swell Neural Network Algorithm for Solving Time-Varying Path Query Problems with Privacy Protection

Abstract

In this paper, a swell neural network (SNN) algorithm was proposed for solving time-varying path query (TVPQ) problems with privacy protection with the following goals: (i) querying the K-nearest paths with time limitations in a time-varying scenario, and (ii) protecting private information from neighborhood attacks. The proposed SNN is a network in which the optimal paths can be calculated at the same time with no need for training. For TVPQ, a node is considered a neuron, and time-varying means that an edge has different costs in different time windows. For SNN, the query paths are swell sets from the start to the target within an upper limit. An encrypted index is designed for privacy protection. The evaluation of the efficiency and accuracy of the SNN was carried out based on New York road instances.