Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective Particle Swarm Optimization

Tian, Cenxi; Xu, Hongyun; Lü, Tao; Jiang, Rui; Kuang, Yong

doi:10.1109/access.2021.3071407

Cited by 13 publications

(17 citation statements)

References 45 publications

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“…An implementation of location perturbation is to blur the actual location into an area. For example, Tian et al [13] investigate the problem of privately releasing location data in an online manner. Given an actual location at some time instant, the authors blur the location into an area containing other k − 1 locations, which have similar geographical and semantic utility with the actual location.…”

Section: Location Perturbationmentioning

confidence: 99%

“…It is the negative of the greatest of all the distances from points in one set to the closest point in the other set. Equation (13) presents the definition of negative Hausdorff distance between the actual dataset D A and obfuscated D O :…”

Section: Geographic Utility Metricsmentioning

confidence: 99%

“…Existing work cannot provide a formal and provable privacy guarantee to thwart the privacy attacks. For instance, Tian et al [13] protects privacy by blurring the actual location into a k-anonymous area. Since there is not provable correlation between the privacy parameter k and the privacy guarantee, the data curator cannot determine k for a specific scenario.…”

Section: Introductionmentioning

confidence: 99%

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Releasing Differentially Private Trajectories with Optimized Data Utility

Zhu

Xie

2022

Applied Sciences

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The ubiquity of GPS-enabled devices has resulted in an abundance of data about individual trajectories. Releasing trajectories enables a range of data analysis tasks, such as urban planning, but it also poses a risk in compromising individual location privacy. To tackle this issue, a number of location privacy protection algorithms are proposed. However, existing works are primarily concerned with maintaining the trajectory data geographic utility and neglect the semantic utility. Thus, many data analysis tasks relying on utility, e.g., semantic annotation, suffer from poor performance. Furthermore, the released trajectories are vulnerable to location inference attacks and de-anonymization attacks due to insufficient privacy guarantee. In this paper, to design a location privacy protection algorithm for releasing an offline trajectory dataset to potentially untrusted analyzers, we propose a utility-optimized and differentially private trajectory synthesizer (UDPT) with two novel features. First, UDPT simultaneously preserves both geographical utility and semantic utility by solving a data utility optimization problem with a genetic algorithm. Second, UDPT provides a formal and provable guarantee against privacy attacks by synthesizing obfuscated trajectories in a differentially private manner. Extensive experimental evaluations on real-world datasets demonstrate UDPT’s outperformance against state-of-the-art works in terms of data utility and privacy.

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Section: Location Perturbationmentioning

confidence: 99%

Section: Geographic Utility Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Releasing Differentially Private Trajectories with Optimized Data Utility

Zhu

Xie

2022

Applied Sciences

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“…Then another author proposed a novel method of location privacy protection based on geographic semantics while satisfying k-anonymity [11], in which the candidate set was constructed by using the maximum and minimum distance multi-centers clustering algorithm, and the virtual location result set was generated according to its semantic similarity. the authors of [12] constructed a semantic and trade-off aware location privacy protection mechanism (STA-LPPM), in which the multi-objective particle swarm optimization (MOPSO) algorithm was used to generate the optimal anonymous set to achieve the balance between privacy protection and quality of service. A blockchain-enabled framework for P2P energy trading was designed in [13], and an Anonymous Proof of Location algorithm is proposed that allows clients to choose their trading partners without revealing their real location.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

Wang

Guo

et al. 2022

Preprint

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Location-based services (LBSS) are widely used with the rapid development of mobile devices and location technology. LBSs not only brings convenience to life, but also brings privacy threats to users. Users upload accurate location information to LBSs to obtain the corresponding services. However, uploading unprocessed location data will directly cause the leakage of users’ privacy information. In this work, an efficient location protection method based on differential privacy is proposed. First of all, the users’ locations information is merged based on the distance and density relationship between multiple groups of continuous locations, and a location clustering (L-cluster) algorithm is proposed to divide the continuous locations into various clusters. Then, differential privacy based location protection algorithm (DPLPA) is proposed to efficiently preserve users’ location privacy, where Laplace noise conforming to the differential privacy mechanism is added to resident points and centroids in the cluster to protect the location privacy. Experimental results show that DPLPA gets high data utility and low time consumption while protecting the privacy of location information.

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“…Crowdsensing is a new mobile sensing computing paradigm that extends current wireless sensing networks for mobile networks and the Internet of Things [1,2]. At the same time, crowdsensing is very important to protect the privacy of user activity information during the extension of wireless sensing networks.…”

Section: Introductionmentioning

confidence: 99%

Stability Control of Position Flow Fuzzy Estimation in Swarm Intelligence Aware Privacy Protection

Zhang

Wang

2023

Wireless Communications and Mobile Computing

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The group intelligence perception privacy protection model is a method to achieve the balance between user privacy and service requests through the cooperation between users using location services and has a good perception effect. In order to better protect the location privacy of network users and improve the stability control effect of fuzzy estimation of location flow, this paper designs a stability control method of fuzzy estimation of location flow in group intelligent perception privacy protection. This method uses the group intelligence aware privacy protection model to obtain the user network location coordinates in the group intelligence aware privacy protection. Taking the user’s network location coordinates as input, the location flow queue of multiple users in the group intelligence aware privacy protection network is obtained by the Lyapunov multiobjective location flow estimation queue model. After the fuzzy processing of the user location flow queue, the online control mechanism of location flow fuzzy estimation stability under different conditions is established. According to the online control mechanism, a stability control method based on access control and group intelligence aware task allocation is used to realize the stability control of location flow fuzzy estimation in group intelligence aware privacy protection. The experimental results show that the method can obtain 100% of the user location integrity in the group intelligence aware privacy protection, and the target location flow estimation queue is more accurate. It can effectively reduce the number of communication rounds of fuzzy estimation of location flow in the group intelligence aware privacy protection and has better stability control ability.

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Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective Particle Swarm Optimization

Cited by 13 publications

References 45 publications

Releasing Differentially Private Trajectories with Optimized Data Utility

Releasing Differentially Private Trajectories with Optimized Data Utility

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

Stability Control of Position Flow Fuzzy Estimation in Swarm Intelligence Aware Privacy Protection

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