Poisoning Attacks in Crowdsensing Over Multiple Areas

Mobile crowdsensing (MCS) using mobile devices has been gaining attention as a low-cost way to estimate various environmental data. In one type of MCS service, the service platform estimates environmental data, such as temperature or carbon dioxide concentration, from data reported by the mobile devices of crowdsensing workers. However, due to the nature of collecting data from such varied and unknown users, there is a risk of data poisoning attacks, in which malicious workers intentionally send data with large errors to increase the estimation error of data. While existing studies have investigated data poisoning attacks and methods of preventing them assuming a single area, data poisoning attacks on multiple areas have not been studied. In this paper, we propose strategies for data poisoning attacks in MCS across multiple areas in which the environmental value is estimated at each area independently. The proposed methods optimize the number of malicious workers allocated to each area to maximize the effectiveness of the attacker. Using computer simulations, we show that the proposed methods generate errors in many areas and increase the total error among all areas.