Sensitive Data Privacy Protection of Carrier in Intelligent Logistics System

Abstract

An intelligent logistics system is a production system based on the Internet of Things (IoT), and the logistics information of humans has a high degree of privacy. However, the current intelligent logistics system only protects the privacy of shippers and consignees, without any privacy protection for carriers, which will not only cause carriers’ privacy leakage but also indirectly or directly affect the logistics efficiency. It is particularly worth noting that solving this problem requires one to consider the balance between privacy protection and operational visibility. So, the local privacy protection algorithm ϵ-L_LDP for carriers’ multidimensional numerical sensitive data and ϵ-LT_LDP for carrier location sensitive data are proposed. For ϵ-L_LDP, firstly, a personalized and locally differentiated privacy budgeting approach is used. Then, the multidimensional data personalization perturbation mechanism algorithm L-PM is designed. Finally, the multidimensional data are perturbed using L-PM. For ϵ-LT_LDP, firstly, the location area is matrix-partitioned and quadtree indexed, and the location data are indexed according to the quadtree to obtain the geographic location code in which it is located. Secondly, the personalized random response perturbation algorithm L-RR for location trajectory data is also designed. Finally, the L-RR algorithm is used to implement the perturbation of geolocation-encoded data. Experiments are conducted using real and simulated datasets, the results show that the ϵ-L_LDP algorithm and ϵ-LT_LDP algorithm can better protect the privacy information of carriers and ensure the availability of carrier data during the logistics process. This effectively meets the balance between the privacy protection and operational visibility of the intelligent logistics system.