A novel attributes anonymity scheme in continuous query

Zhang, Lei; Li, Jing; Yang, Songtao; Wang, Bin; Bian, Xingchao

doi:10.1007/s11277-018-5735-0

Cited by 3 publications

(5 citation statements)

References 29 publications

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“…The en-DLS algorithm [20] selects the dummy variables from locations that have similar query probability to the current location. SACU [21] is an attribute encryption algorithm that requests the establishment of a cooperative group by broadcast and combines the cache strategy. The performances of the five algorithms in entropy measurement, area generation of anonymity, communication cost are compared by experimental simulation.…”

Section: Methodsmentioning

confidence: 99%

“…), the attacker can quickly identify the exact location of the real user. As an addition to k -anonymity, entropy [19,21,22,25,26] can also measure the extent of privacy protection. The communication framework based on the information theory [27,28] proposed the information entropy model for privacy protection, which refers to the probability that an attacker can identify the real user among other k − 1 users.…”

Section: Location Privacy Metricsmentioning

confidence: 99%

“…In recent years, the issue of protecting users' location privacy has been widely concerned. In addition to policybased and cryptographic-based algorithms, space-time stealthy [4,5,29,30], location confusion [7,10,[13][14][15][16][17]22] and dummy locations [8,21,27] are widely utilized. At present, fuzzy user's real location is the most commonly used and most effective location privacy protection algorithms.…”

Section: Location Privacy Mechanismsmentioning

confidence: 99%

“…In addition, in the process of continuous query, it is easy for attackers to associate discrete locations to tracks, and reveal more information through the user's movement track, until the user's privacy. Zhang et al [21] proposed an attribute encryption algorithm, in which enough anonymous users are found by broadcast query, and utilized the cache strategy. The algorithm can effectively resist the inference attack of LBS server, but the problem of cache update is not considered in the algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A new grid partitioning technology for location privacy protection

2020

Turk J Elec Eng & Comp Sci

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Nowadays, the location-based service (LBS) has become an essential part of convenient service in people's daily life. However, the untrusted LBS servers can store lots of information about the user, such as the user's identity, location, and destination. Then the information can be used as background knowledge and combined with the query frequency of the user to launch the inference attack to obtain user's privacy. In most of the existing schemes, the author considers the algorithm of virtual location selection from the historical location of the user. However, the LBS server can infer the user's location information on the historical data that has been counted for a long time. In order to ensure that the users' historical query data and query frequency will not be obtained by the attacker, we propose a privacy protection algorithm based on grid expansion. With the help of third-party agents, the combination of cooperative users and pseudonyms can resist the privacy disclosure caused by users requesting services during the mobile process.Extensive simulation experiments have been carried out on Gowalla dataset to evaluate the efficiency of the proposed algorithm. By comparing with other existing methods, the experimental verify the effectiveness of our algorithm in privacy protection.

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Section: Methodsmentioning

confidence: 99%

Section: Location Privacy Metricsmentioning

confidence: 99%

Section: Location Privacy Mechanismsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new grid partitioning technology for location privacy protection

2020

Turk J Elec Eng & Comp Sci

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“…ere are Taobao for dressing, Meituan for eating, Flying Pig for lodging, Didi for travelling, and strips for travelling. People can get services anytime and anywhere through various apps [1], all of which are derived from the rapid development of Location-Based Services (LBS). According to statistics [2], the global market share of LBS and Real-Time Location Systems (RTLS) will increase from 11.36 billion in 2015 to 54.95 billion US dollars in 2020, and the Compound Annual Growth (CAGR) will be 37.1%.…”

Section: Introductionmentioning

confidence: 99%

KPDR : An Effective Method of Privacy Protection

Shen

Wei

et al. 2021

Complexity

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To solve the problem of user privacy disclosure caused by attacks on anonymous areas in spatial generalization privacy protection methods, a K and P Dirichlet Retrieval (KPDR) method based on k-anonymity mechanism is proposed. First, the Dirichlet graph model is introduced, the same kind of information points is analyzed by using the characteristics of Dirichlet graph, and the anonymous set of users is generated and sent to LBS server. Second, the relationship matrix is generated, and the proximity relationship between the user position and the target information point is obtained by calculation. Then, the private information retrieval model is applied to ensure the privacy of users’ target information points. Finally, the experimental results show that the KPDR method not only satisfies the diversity of l 3 / 4 , but also increases the anonymous space, reduces the communication overhead, ensures the anonymous success rate of users, and effectively prevents the disclosure of user privacy.

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Exchanging registered users’ submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks

Wang

Xin

et al. 2021

PLoS ONE

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In Location-Based Social Networks (LBSNs), registered users submit their reviews for visited point-of-interests (POIs) to the system providers (SPs). The SPs anonymously publish submitted reviews to build reputations for POIs. Unfortunately, the user profile and trajectory contained in reviews can be easily obtained by adversaries who SPs has compromised with. Even worse, existing techniques, such as cryptography and generalization, etc., are infeasible due to the necessity of public publication of reviews and the facticity of reviews. Inspired by pseudonym techniques, we propose an approach to exchanging reviews before users submit reviews to SPs. In our approach, we introduce two attacks, namely review-based location correlation attack (RLCA) and semantic-based long-term statistical attack (SLSA). RLCA can be exploited to link the real user by reconstructing the trajectory, and SLSA can be launched to establish a connection between locations and users through the difference of semantic frequency. To resist RLCA, we design a method named User Selection to Resist RLCA (USR-RLCA) to exchange reviews. We propose a metric to measure the correlation between a user and a trajectory. Based on the metric, USR-RLCA can select reviews resisting RLCA to exchange by suppressing the number of locations on each reconstructed trajectory below the correlation. However, USR-RLCA fails to resist SLSA because of ignoring the essential semantics. Hence, we design an enhanced USR-RLCA named User Selection to Resist SLSA (USR-SLSA). We first propose a metric to measure the indistinguishability of locations concerning the difference of semantic frequency in a long term. Then, USR-SLSA can select reviews resisting SLSA to exchange by allowing two reviews whose indistinguishability is below the probability difference after the exchange to be exchanged. Evaluation results verify the effectiveness of our approach in terms of privacy and utility.

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A novel attributes anonymity scheme in continuous query

Cited by 3 publications

References 29 publications

A new grid partitioning technology for location privacy protection

A new grid partitioning technology for location privacy protection

KPDR : An Effective Method of Privacy Protection

Exchanging registered users’ submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks

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