Multi-factor Dummy Location Selection Algorithm in Location-based Service

In view of the existing dummy location selection methods in LBS snapshot location privacy protection,the background knowledge attack caused by the time factor of the location itself is ignored,and the sensitive locations are treated equally.Based on this,a multi-factor dummy location selection algorithm(MFDLS) is proposed,which comprehensively considers the factors that affect privacy leakage,including background knowledge such as geographical attributes,semantic attributes,time attributes of the location and query probability as well as the users' sensitive preferences.To ensure that the selected dummy locations can not only effectively resist location homogeneity attack,location semantic attack and query probability distribution attack,but also deal with multiple threats such as location distribution attack,sensitive homogeneity attack and link attack.The algorithm selects the dummy locations that meet the requirements of query probability close to the initiating time,semantic diversification,large anonymous space and relatively consistent time,non-outlier and central point.Compared with the existing dummy location selection algorithm,the security analysis and simulation results show that the proposed algorithm improves the adversary error by at least 16% and reduces the quality loss by at least 30%,which can more effectively resist the background knowledge attack and meet the users' privacy requirements.