Your trajectory privacy can be breached even if you walk in groups

Sui, Kaixin; Zhao, Youjian; Liu, Dapeng; Ma, Minghua; Xu, Lei; Li, Zimu; Pei, Dan

doi:10.1109/iwqos.2016.7590444

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…In the example of Muslim taxi drivers mentioned above, the attacker inferred an attribute: the victims' religion, even though they did not identify anyone's trajectory. Sui et al [102] observe that 40% of the records that cannot be immediately identified in their data and seem anonymous were instead homogeneous and directly disclose the shared attribute.…”

Section: Attribute Disclosurementioning

confidence: 99%

SoK: Differentially Private Publication of Trajectory Data

Miranda-Pascual

Guerra-Balboa

Parra-Arnau

et al. 2023

PoPETs

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Trajectory analysis holds many promises, from improvements in traffic management to routing advice or infrastructure development. However, learning users' paths is extremely privacy-invasive. Therefore, there is a necessity to protect trajectories such that we preserve the global properties, useful for analysis, while specific and private information of individuals remains inaccessible. Trajectories, however, are difficult to protect, since they are sequential, highly dimensional, correlated, bound to geophysical restrictions, and easily mapped to semantic points of interest. This paper aims to establish a systematic framework on protective masking and synthetic-generation measures for trajectory databases with syntactic and differentially private (DP) guarantees, including also utility properties, derived from ideas and limitations of existing proposals. To reach this goal, we systematize the utility metrics used throughout the literature, deeply analyze the DP granularity notions, explore and elaborate on the state of the art on privacy-enhancing mechanisms and their problems, and expose the main limitations of DP notions in the context of trajectories.

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Section: Attribute Disclosurementioning

confidence: 99%

SoK: Differentially Private Publication of Trajectory Data

Miranda-Pascual

Guerra-Balboa

Parra-Arnau

et al. 2023

PoPETs

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“…A more sophisticated attack called semantic attack was proposed by Sui et al [12] in 2016. In this threat model, the attacker can query the semantic information from the trajectory data and infer the victim's behavior when combining POI distribution on the map.…”

Section: Privacy Risks On Trajectory Datamentioning

confidence: 99%

A Semantically Sensitive Privacy Protection Method for Trajectory Publishing

Shao¹,

Feng²,

Li³

2021

JCC

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Trajectory data set is the indispensable foundation for constructing reliable Internet of Vehicles (IoV) service and location-based service (LBS), while it is likely to be abused by malicious attackers to infer user's privacy. In this paper, we propose a trajectory protection method based on stop points obfuscation, which can confront various privacy attacks and preserve the semantic information to achieve adequate utility. Two strategies for stop point selection are designed, including category-distance priority method and Markov matrix method. Our new method was analyzed and evaluated on a real-world trajectory data set. The experiment result shows that our method can improve the utility of the data set and provide multi-level privacy protection.

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“…e generalization method is less effective in providing suitable protection for user privacy in the face of single trajectory data with sensitive attributes. Sui et al [13] analyzed the risk of trajectory data in a campus with a single trajectory diversity and provided quantitative proofs of the low diversity risk in the data. eir experiments demonstrate that, in the case of low user privacy diversity, personalized privacy protection methods are needed to compensate for the risk of privacy leakage caused by the lack of diversity.…”

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

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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.

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Your trajectory privacy can be breached even if you walk in groups

Cited by 14 publications

References 19 publications

SoK: Differentially Private Publication of Trajectory Data

SoK: Differentially Private Publication of Trajectory Data

A Semantically Sensitive Privacy Protection Method for Trajectory Publishing

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

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