A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

Abstract

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.

The Research of Mobile Location Privacy Protection Access Control Method Based on Game Theory

“…This method extends the original sequence database of anonymous datasets. It adopts specific generalization and avoidance principles to gradually hide privacy-sensitive rules and resist reasoning attacks [10]. There is also someone in conjunction with other technologies for privacy protection research.…”

“…Incomes(R) is the expected value of the income when the system adopts the deny access policy. Informef(G)> Incomef(E) can be derived from (9) and (10). That is, regardless of the probability of a good-faith visit of the visitor, the visitor's good-faith access behaviour will have a greater expected total return value than a malicious visit.…”

Batching Location Cloaking Techniques for Location Privacy and Safety Protection

“…A cloaking region needs to contain a user's current position and also encloses other locations in which the user could be located. Most of the approaches [2][3][4][5][6][7][8][9][10][11] are based on a trusted third party, called the anonymizer, which is responsible for selecting these additional locations depending on what type of protection a user is demanding. Other techniques such as [12][13][14][15][16] assume that the same users, collaborating with other peers, can compute their own cloaking regions.…”

“…(2) if SF(C N ) ≥ θ max then (3) repeat (4) l ⟵ a user from U with the largest θ. If many, chooses the one with the largest K, (K l ); (5) CR l ⟵ C N ; (6) repeat (7) E max ⟵ 0; (8) i ⟵ 0; (9) for i < m do (10) c ⟵ from CR l \ c l with a probability ∝ cell's occupancy; (11) if…”

Markov and improved particle swarm optimization-based privacy preservation algorithm for user space geographical location

“…In document (Liu, Bai, Wang, & Li, 2016), to protect user identity and location information, Central anonymous serve not only uses information perturbation strategy and space-time location anonymity algorithm, but also considers the balance between QoS and CR. In (Zhang, Wu, Chen, & Chen, 2017), a spatio-temporal anonymity algorithm is proposed to protect users' location privacy by using k-anonymity and pseudo-identity anonymity. In (Wang, Huang, Qin, Wang, & Wu, 2017), a weighted k-anonymity algorithm for adjacency graphs is proposed; this algorithm can not only protect the privacy of the user's location, but also reduce bandwidth.…”