k-Trustee: Location injection attack-resilient anonymization for location privacy

Abstract

Cloaking-based location privacy preserving mechanisms have been widely adopted to protect users' location privacy when using location-based services. A fundamental limitation of such mechanisms is that users and their location information in the system are inherently trusted by the Anonymization Server without any verification. In this paper, we show that such an issue could lead to a new class of attacks called location injection attacks which can successfully violate users' in-distinguishability (guaranteed by k-Anonymity) among a set of users. We propose and characterize location injection attacks by presenting a set of attack models and quantify the costs associated with them. We then propose and evaluate k-Trustee, a trust-aware location cloaking mechanism that is resilient to location injection attacks and guarantees a lower bound on the user's in-distinguishability. k-Trustee guarantees that each user in a given cloaked region can achieve the required privacy level of k-Anonymity by including at least k-1 other trusted users in the cloaked region. We demonstrate the effectiveness of k-Trustee through extensive experiments in a real-world geographic map and our experimental results show that the proposed cloaking algorithm guaranteeing k-Trustee is effective against various location injection attacks.

Location Privacy Protection Research Based on Querying Anonymous Region Construction for Smart Campus

“…In [32], the anonymous server maps all users in two-dimensional space to a onedimensional array according to the Hilbert curve and divides the users into several sets according to the value of k. When a user makes a service request, the anonymous region is constructed by using the user set to which the user belongs. Recently, Jin et al [2] thought the anonymization server could lead to a new class of attacks called location injection attacks which can successfully violate users' indistinguishability (guaranteed by k-anonymity) among a set of users.…”

“…Examples of such risks include spamming users with unwanted advertisements, drawing sensitive inferences from victims' visits to various locations (e.g., students and teachers' offices), and learning sensitive information about them (identity, religious and political affiliations, etc.). Hence, location privacy protection for smart campus is becoming a critical issue [2].…”

“…(1) Based on the theoretical analysis, it is concluded that the existing anonymous region demarcation method cannot reduce the LSP querying cost and improve the service quality, and we prove it through experiments. (2) We think that the area of querying regions is the judgment criterion to merge the anonymous subregions and propose an anonymous region construction scheme based on the user's query range. Security analysis shows that the proposed scheme can effectively protect the users' location privacy.…”

Have The Cake And Eat It Too: Differential Privacy Enables Privacy And Precise Analytics

“…In the last few years, there has been an explosion of research articles that apply differential privacy to various functional areas such as healthcare [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], learning [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], location-based services [39][40][41][42][43][44][45][46][47], internet-based collaboration [48], Internet of Things [49][50][51], block-chains [52][53][54], cyber-physical systems [55][56][57]…”