A Privacy-Preserving Location-Based System for Continuous Spatial Queries

Abstract

K-anonymization generated a cloaked region (CR) that was K-anonymous; that is, the query issuer was indistinguishable from K-1 other users (nearest neighbors) within the CR. This reduced the probability of the query issuer’s location being exposed to untrusted parties (1/K). However, location cloaking is vulnerable to query tracking attacks, wherein the adversary can infer the query issuer by comparing the two regions in continuous LBS queries. This paper proposes a novel location cloaking method to resist this attack. The target systems of the proposed method are road networks where the mobile clients’ trajectories are fixed (the road network is preknown and fixed, instead of the trajectories), such as subways, railways, and highways. The proposed method, called adaptive-fixed K-anonymization (A-KF), takes this issue into account and generates smaller CRs without compromising the privacy of the query issuer’s location. Our results show that the proposed A-KF method outperforms previous location cloaking methods.

An Advanced Dummy Position-Based Privacy Provisioning Framework for TTP-Based LBS System

“…The simulation scenario in this portion was defined, and then the outcomes of the simulations were interpreted. The results were also compared with the fixed K-anonymization (Kf) [27] and active fixed K-anonymization (A-Kf) [28]. The simulation tool, Riverbed Modeler [37], was used to execute the simulations.…”

“…Currently, the PLAM algorithms based on ldiversity and k-anonymity need to be checked on the route, unless there are more uncontrollable factors to be tested, such as the 4G network power. In [27], the authors elaborated on the location-cloaking approach to avoid the occurrence of aggression and to reduce the possibility of disclosing the position of the requesting issuer to untrusted parties (1/k). Location cloaking is accessible to query tracking attacks, where the opponent can determine the querier by comparing the two sectors in the LBS queries.…”

“…Certainly, existing methodologies consistently ensure user privacy using established privacy metrics such as kanonymity or entropy. Additionally, with the advent of blockchain, recent works [27][28][29][30][31][32] have merged LBS and blockchain technologies to enhance location privacy, marking a noteworthy and growing trend. However, existing approaches exhibit certain limitations.…”

Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark

“…In [24] a fast continuous query privacy-preserving framework in road networks is recommended, based on the concepts of both k-anonymity and l-diversity. The authors in [25] propose a method to make location cloaking less vulnerable to query tracking attacks. The proposed method is applied on road networks, such as subways, railways, and highways, where the road network is known and fixed, except for the trajectories.…”