PEAK: Privacy-Enhanced Incentive Mechanism for Distributed <i>K</i>-Anonymity in LBS

Man, Zhang; Li, Xinghua; Miao, Yinbin; Luo, Bin; Ren, Yanbing; Ma, Siqi

doi:10.1109/tkde.2023.3295451

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…This formulation encapsulates the interplay between user beliefs, protection mechanisms, and the spatial, semantic, and temporal context in determining the quality loss. Formula (6) shows that the more consistent the semantics between the true location of the user and the generated perturbation location are, the more consistent the temporal properties are and the less quality loss is.…”

Section: Evaluation Indexmentioning

confidence: 99%

“…Researchers have approached the challenge of location-based privacy protection from various perspectives. Pseudonym techniques [ 2 ], dummy location methods [ 3 ], encryption techniques [ 4 ], and anonymity techniques [ 5 , 6 ] have been employed to safeguard user privacy. Pseudonymous queries [ 7 ] have also been utilized to protect query privacy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors

Li,

Zhu,

Fei

et al. 2024

Sensors

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Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors’ preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection.

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Section: Evaluation Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors

Li,

Zhu,

Fei

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, the model pays full attention to users’ characteristics and requirements, exhibits strong scalability, and proves to be well suited to practical application scenarios. To further enhance the privacy protection effect of trajectories, this research [ 45 ] proposes the Privacy-Enhanced Distributed k -Anonymous Reward Mechanism (PEAK) to incentivize users to participate in distributed k -anonymity privacy protection in location services. PEAK does not need to trust third parties, establishes anonymous zones through currency transactions and location transmission, introduces role recognition and accountability mechanisms to limit malicious users, improves security and feasibility, and successfully establishes anonymous zones with a success rate of over 0.9, significantly reducing the utility of malicious users.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

Shen,

Wang,

Zhang

2023

Sensors

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With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory k-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are n similar point sets, each consisting of m points. The size of the space is then mn. Furthermore, to choose suitable k− 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a k-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k−1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving k-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.

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“…LBS refers to obtaining the specific geographical location coordinates of users in the mobile Internet through positioning technology, and the location service provider (LSP) provides the user with corresponding information query, entertainment games and other related mobile Internet services [1]. In real-life scenarios, LBS is commonly used in applications such as maps and navigation, local searches, and location-based advertising.…”

Section: Introductionmentioning

confidence: 99%

A Method for Generating Confusing Positions to Resist Long-term Observation Attacks

2024

AJCIS

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With the upgrading of mobile smart devices and rapid innovation of positioning technology, the widespread application of Location Based Services (LBS) has been propelled. However, users face the threat of privacy leaks while using LBS, especially when their behaviors over a long period of time are collected and stored. The accumulated information can be exploited by adversaries, greatly increasing the risk of privacy leakage. This type of attack is known as long-term observation attack. How to better protect users' location privacy remains a challenging issue. In this paper, we propose a method for generating obfuscated locations to counter long-term observation attacks. Firstly, we determine the output set based on the user's quality of service requirements and custom sampling probability. Then, we introduce an improved quadtree structure to store user location information and select users with similar destinations to form an anonymous set. Finally, noise is added to prevent long-term observers from accurately obtaining the user's precise location. Experimental results demonstrate that our method can effectively protect users from long-term observation attacks while maintaining low time cost and high anonymity efficiency.

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PEAK: Privacy-Enhanced Incentive Mechanism for Distributed K-Anonymity in LBS

Cited by 5 publications

References 31 publications

Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors

Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

A Method for Generating Confusing Positions to Resist Long-term Observation Attacks

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