Sensitive Semantics-Aware Personality Cloaking on Road-Network Environment

Li, Min; Qin, Zhiguang

doi:10.14257/ijsia.2014.8.1.13

Cited by 13 publications

(8 citation statements)

References 26 publications

(52 reference statements)

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“…In [ 20 ], the regional division is improved by adopting a multi-level grid model to divide position points at different speeds in the trajectory according to different granularity, so as to maintain the original sequence information of the trajectory to the maximum extent. However, these methods have the problem of low data availability due to excessive information loss rate, and fail to fully consider the semantic location information of users, resulting in semantic inference attacks [ 21 ], which leads to the disclosure of users’ sensitive privacy.…”

Section: Related Workmentioning

confidence: 99%

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Zhang

Ding

et al. 2022

Entropy

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The publication of trajectory data provides critical information for various location-based services, and it is critical to publish trajectory data safely while ensuring its availability. Differential privacy is a promising privacy protection technology for publishing trajectory data securely. Most of the existing trajectory privacy protection schemes do not take into account the user’s preference for location and the influence of semantic location. Besides, differential privacy for trajectory protection still has the problem of balance between the privacy budget and service quality. In this paper, a semantics- and prediction-based differential privacy protection scheme for trajectory data is proposed. Firstly, trajectory data are transformed into a prefix tree structure to ensure that they satisfy differential privacy. Secondly, considering the influence of semantic location on trajectory, semantic sensitivity combined with location check-in frequency is used to calculate the sensitivity of each position in the trajectory. The privacy level of the position is classified by setting thresholds. Moreover, the corresponding privacy budget is allocated according to the location privacy level. Finally, a Markov chain is used to predict the attack probability of each position in the trajectory. On this basis, the allocation of the privacy budget is further adjusted and its utilization rate is improved. Thus, the problem of the balance between the privacy budget and service quality is solved. Experimental results show that the proposed scheme is able to ensure data availability while protecting data privacy.

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Section: Related Workmentioning

confidence: 99%

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Zhang

Ding

et al. 2022

Entropy

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“…In recent years, domestic and foreign researchers and institutions have widespread attention on location-based privacy preservation; increasingly in-depth studies are carried out in location-based privacy. Besides the above-described location privacy preserving technologies, there are a wealth of methods such as location data randomization [32], fuzzification of space or time data [33,34], methods based on strategies and encryption [35][36][37], and sensitive semantic based security anonymity mechanism [33,[38][39][40].…”

Section: Related Workmentioning

confidence: 99%

RPAR: Location Privacy Preserving via Repartitioning Anonymous Region in Mobile Social Network

Zhang

Yuan

Wang

et al. 2018

Security and Communication Networks

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Applying the proliferated location-based services (LBSs) to social networks has spawned mobile social network (MSN) services that allow users to discover potential friends around them. While enjoying the convenience of MSN services, the mobile users also are confronted with the risk of location disclosure, which is a severe privacy preserving concern. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a repartitioning anonymous region for location privacy preserving (RPAR) scheme based on the central anonymous location which minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location. Furthermore, our scheme enables the users to get more accurate query results, thus improving the quality of the location service. Simulation results show that our proposed scheme can effectively reduce the area of anonymous regions and minimize the traffic.

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“…Spatial cloaking [17][18][19][20][21][22][23][24][25] is a fairly popular mechanism. Chow et al [17] propose Casper cloak algorithm, which uses a quad-tree data structure and allows users to determine the size of and the minimum anonymous area, but the privacy can be guaranteed only when users' positions are distributed evenly.…”

Section: Related Workmentioning

confidence: 99%

“…It is common to generate Security and Communication Networks add query count to Sets (6) end if (7) end for (8) e Sets are randomly divided into set 1 and set 2 equally (9) Do (10) akg 1 = average(set 1 ); akg 2 = average(set 2 ); (11) for in Sets (12) if (s − akg 1 ) 2 < (s − akg 2 ) 2 then (13) s belong to c 1 (14) else (15) s belong to c 2 (16) end if (17) end for (18) while there are changes on the elements in c 1 and c 2 (19) if the number of GID belongs to c 1 then (20) FCR isselected randomly from the regions with the number in c 1 except RCR (21) else FCR is selected randomly from the regions with the number in c 2 except RCR (22) dummy positions randomly in the double cloaking regions; however, we propose defining two rules to generate fixed dummy positions according to . In Figure 8, the red solid circle represents the user's real position and the solid circles represent the fixed dummy positions according to our rules, while the dotted circles represent the dummy positions generated randomly.…”

Section: Dummies Generation Algorithmmentioning

confidence: 99%

An Improved Privacy-Preserving Framework for Location-Based Services Based on Double Cloaking Regions with Supplementary Information Constraints

Kuang

Wang

et al. 2017

Security and Communication Networks

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With the rapid development of location-based services in the field of mobile network applications, users enjoy the convenience of location-based services on one side, while being exposed to the risk of disclosure of privacy on the other side. Attacker will make a fierce attack based on the probability of inquiry, map data, point of interest (POI), and other supplementary information. The existing location privacy protection techniques seldom consider the supplementary information held by attackers and usually only generate single cloaking region according to the protected location point, and the query efficiency is relatively low. In this paper, we improve the existing LBSs system framework, in which we generate double cloaking regions by constraining the supplementary information, and then k-anonymous task is achieved by the cooperation of the double cloaking regions; specifically speaking, k dummy points of fixed dummy positions in the double cloaking regions are generated and the LBSs query is then performed. Finally, the effectiveness of the proposed method is verified by the experiments on real datasets.

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Sensitive Semantics-Aware Personality Cloaking on Road-Network Environment

Abstract: Recently, several cloaking methods based on K-anonymity and L-diversity has been proposed to protect the user's location privacy for Location-based Services (LBS)

Cited by 13 publications

References 26 publications

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

RPAR: Location Privacy Preserving via Repartitioning Anonymous Region in Mobile Social Network

An Improved Privacy-Preserving Framework for Location-Based Services Based on Double Cloaking Regions with Supplementary Information Constraints

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