Give Me Two Points and I'll Tell You Who You Are

Mattos, Ekler Paulino de; Domingues, Augusto C. S. A.; Loureiro, Antônio A. F.

doi:10.1109/ivs.2019.8814250

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…Known locations [28], [29], [30] Location probability distribution [31], [32], [33], [34], [35] POI / Personal gazetteer [36] Shared path [37] Personal profile…”

Section: Record Linkagementioning

confidence: 99%

“…Record Linkage: Record linkage is the mainstream attack addressed by the state-of-art contributions. An adversary with some background knowledge (e.g., exposed locations [28], [29], origin and destination locations [30], and social relationships [60]) can try to identify the record of a known victim (i.e., perform a re-identification attack). In [35], linkage is formalized as a k-nearest-neighbor search (i.e., finding the most similar k individuals to the query one).…”

Section: Linkage Modelsmentioning

confidence: 99%

“…The length of the region is proportional to the average road-segment speed, providing the best protection against timing attacks. On the contrary, [30] attempts to figure out the vulnerabilities of Mix-zone methods by conducting an attack under the assumption that moving objects follow the shortest path between origin/destination locations. It claims that an adversary can compare the minimum path between known origin/destination locations using the Dijkstra algorithm in a road network and the minimum DTW distance between the anonymized trajectories.…”

Section: Mix-zonementioning

confidence: 99%

“…Attack Success: Success metrics quantify how effective/accurate an attack model is. For instance, identification accuracy measures how many individuals can be accurately identified (i.e., linked back to the original records) after anonymization [104], [105], [30], [73]. In practice, this kind of metrics mostly depends on the adopted attack model.…”

Section: Privacy Metricsmentioning

confidence: 99%

See 3 more Smart Citations

A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

Jin

Hua

Francia

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.

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“…Known locations [28], [29], [30] Location probability distribution [31], [32], [33], [34], [35] POI / Personal gazetteer [36] Shared path [37] Personal profile…”

Section: Record Linkagementioning

confidence: 99%

Section: Linkage Modelsmentioning

confidence: 99%

Section: Mix-zonementioning

confidence: 99%

Section: Privacy Metricsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

Jin

Hua

Francia

et al. 2022

IEEE Trans. Knowl. Data Eng.

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show abstract

“…However, as trajectories contain sensitive information, directly releasing or sharing them might pose serious privacy threats to individuals. Adversaries can identify individuals, and further infer their home addresses, health status and hobbies [4][5][6] from their trajectories. Therefore, it is crucial to designing effective privacy protection methods to sanitize trajectories before publishing or sharing.…”

Section: Introductionmentioning

confidence: 99%

DP-CSM: Efficient Differentially Private Synthesis for Human Mobility Trajectory with Coresets and Staircase Mechanism

Yao

Han

et al. 2022

IJGI

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Generating differentially private synthetic human mobility trajectories from real trajectories is a commonly used approach for privacy-preserving trajectory publishing. However, existing synthetic trajectory generation methods suffer from the drawbacks of poor scalability and suboptimal privacy–utility trade-off, due to continuous spatial space, high dimentionality of trajectory data and the suboptimal noise addition mechanism. To overcome the drawbacks, we propose DP-CSM, a novel differentially private trajectory generation method using coreset clustering and the staircase mechanism, to generate differentially private synthetic trajectories in two main steps. Firstly, it generates generalized locations for each timestamp, and utilizes coreset-based clustering to improve scalability. Secondly, it reconstructs synthetic trajectories with the generalized locations, and uses the staircase mechanism to avoid the over-perturbation of noises and maintain utility of synthetic trajectories. We choose three state-of-the-art clustering-based generation methods as the comparative baselines, and conduct comprehensive experiments on three real-world datasets to evaluate the performance of DP-CSM. Experimental results show that DP-CSM achieves better privacy–utility trade-off than the three baselines, and significantly outperforms the three baselines in terms of efficiency.

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Dynamic Temporal Mix-Zone Placement Approach for Location-Based Services Privacy

Svaigen

Ramos

Ruiz

et al. 2019

2019 IEEE Latin-American Conference on Communications (LATINCOM)

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Give Me Two Points and I'll Tell You Who You Are

Cited by 12 publications

References 17 publications

A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

DP-CSM: Efficient Differentially Private Synthesis for Human Mobility Trajectory with Coresets and Staircase Mechanism

Dynamic Temporal Mix-Zone Placement Approach for Location-Based Services Privacy

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