A Particle Swarm Optimization Clustering-Based Attribute Generalization Privacy Protection Scheme

Zhang, Lei; Yang, Songtao; Li, Jing; Yu, Lili

doi:10.1142/s0218126618501797

Cited by 19 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, it is widely applied to improve privacy-preserving method efficiency [21] [42-43]. For example, in [43], a PSO anonymization is utilized for accelerating the process of finding similar attributes, and the anonymous users are chosen with similar attributes. Relevantly, [21] realizes the privacy protection via the multiobjective optimization algorithm, i.e., it uses the hybrid elite selection strategy to process user privacy information.…”

Section: Related Workmentioning

confidence: 99%

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

Tian

Lü

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k -anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Tian

Lü

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…e uncertainty of the trajectory motion of the cylinder and another cylinder with the same uncertainty interval meant that the two trajectories cannot be distinguished from each other. Recently, Zhang et al [12] proposed a particle swarm optimization anonymization algorithm for attribute generalization. A Venot diagram is established based on the entropy value to divide user's locations to achieve the generalization of sensitive locations.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points

Zhao

Luo

et al. 2020

Mobile Information Systems

View full text Add to dashboard Cite

By judging whether the start-point and end-point of a trajectory conform to the user’s behavioral habits, an attacker who possesses background knowledge can breach the anonymous trajectory. Traditional trajectory privacy preservation schemes often generate an anonymous set of trajectories without considering the security of the trajectory start- and end-points. To address this problem, this paper proposes a privacy-preserving trajectory publication method based on generating secure start- and end-points. First, a candidate set containing a secure start-point and end-point is generated according to the user’s habits. Second, k−1 anonymous trajectories are generated bidirectionally according to that secure candidate set. Finally, accessibility corrections are made for each anonymous trajectory. This method integrates features such as local geographic reachability and trajectory similarity when generating an anonymized set of trajectories. This provides users with privacy preservation at the k-anonymity level, without relying on the trusted third parties and with low algorithm complexity. Compared with existing methods such as trajectory rotation and unidirectional generation, theoretical analysis and experimental results on the datasets of real trajectories show that the anonymous trajectories generated by the proposed method can ensure the security of trajectory privacy while maintaining a higher trajectory similarity.

show abstract

“…During the process of crowdsensing, the publisher does not need to arrive at the sensing place and can get the update sensing information in real time, and the applicant can obtain a certain number of rewards from the publisher . As a result, both the publisher and the applicant will benefit from each other, as the publisher will get the sensing information with a lower expenditure on information collection and the applicant will get an incentive with just a bit of sensing information transformation [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation

Zheng¹,

Yuan²,

Wang³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In the process of crowdsensing, tasks allocation is an important part for the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may aware the location of each other, and then threaten the privacy of them. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the task matching with applicants and publisher also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publisher can be preserved. At last, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. At the last part of this paper, two types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of proposed scheme.

show abstract

A Particle Swarm Optimization Clustering-Based Attribute Generalization Privacy Protection Scheme

Cited by 19 publications

References 22 publications

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

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

A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points

A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation

Contact Info

Product

Resources

About