Protection of sensitive trajectory datasets through spatial and temporal exchange

Naghizade, Elham; Kulik, Lars; Tanin, Egemen

doi:10.1145/2618243.2618278

Cited by 14 publications

(7 citation statements)

References 9 publications

(13 reference statements)

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“…Although the proposed approach is useful for a predefined number of locations, it cannot be generalized to system models in which the users can make queries from an arbitrary location on the map. Naghizadeh et al [26] focused on the stop points along trajectories. A sensitivity measure is introduced in this work, which relies on the amount of time users spend in different locations.…”

Section: Other Anonymization Techniquesmentioning

confidence: 99%

“…Next, Algorithm 1: DynamicSA(tr 1 , tr 2 , H x , H y , H t ). 26 GenT raj ← trace back the code to generate the aligned trajectory 27 ShoT rajStr ← trace back the code to find out structure of shorter trajectory while alignment 28 Return GenT raj, ShoT rajStr, T otLoss we propose a technique that enables the k -means algorithm for clustering the spatiotemporal trajectories and extend our approach for sensitive locations by developing a variation of k -means algorithm that guarantees privacy requirements for all users.…”

Section: Clusteringmentioning

confidence: 99%

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Privacy Preserving Location Data Publishing: A Machine Learning Approach

Shaham

Ding

Liu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to TDrive and Geolife location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

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Section: Other Anonymization Techniquesmentioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

Privacy Preserving Location Data Publishing: A Machine Learning Approach

Shaham

Ding

Liu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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“…Trajectory privacy has been studied extensively in the last decade [4,10,[17][18][19]32]. For example, a study [32] introduces position dummy to hide a user's location by mixing it with fake locations.…”

Section: Trajectory Privacymentioning

confidence: 99%

“…SDD [17] applies the exponential-based randomized mechanism to trajectory data by sampling a rational distance and direction with noises between locations in the trajectory. There are also studies focusing on semantic privacy of trajectories [18,19]. Monreale et al [18] present a place taxonomy based method to preserve the trajectory semantic privacy.…”

Section: Trajectory Privacymentioning

confidence: 99%

Sub-trajectory Similarity Join with Obfuscation

Chang

Tanin

et al. 2021

33rd International Conference on Scientific and Statistical Database Management

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User trajectory data is becoming increasingly accessible due to the prevalence of GPS-equipped devices such as smartphones. Many existing studies focus on querying trajectories that are similar to each other in their entirety. We observe that trajectories partially similar to each other contain useful information about users' travel patterns which should not be ignored. Such partially similar trajectories are critical in applications such as epidemic contact tracing. We thus propose to query trajectories that are within a given distance range from each other for a given period of time. We formulate this problem as a sub-trajectory similarity join query named as the STS-Join. We further propose a distributed index structure and a query algorithm for STS-Join, where users retain their raw location data and only send obfuscated trajectories to a server for query processing. This helps preserve user location privacy which is vital when dealing with such data. Theoretical analysis and experiments on real data confirm the effectiveness and the efficiency of our proposed index structure and query algorithm. CCS CONCEPTS• Information systems → Spatial-temporal systems.

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“…First, the publishers set a unified privacy protection level for all trajectories [6][7][8][9][10][11] and then complete the privacy protection for publishing trajectories. Second, trajectory points are taken as atomic operation objects; the publisher sets a unified privacy protection level for all trajectory points [12][13][14][15][16] and then completes the privacy protection. Wang.…”

Section: Related Workmentioning

confidence: 99%

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Wang

Chen

Zhang

2020

Mathematical Problems in Engineering

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Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.

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Protection of sensitive trajectory datasets through spatial and temporal exchange

Cited by 14 publications

References 9 publications

Privacy Preserving Location Data Publishing: A Machine Learning Approach

Privacy Preserving Location Data Publishing: A Machine Learning Approach

Sub-trajectory Similarity Join with Obfuscation

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

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