Anonymization of moving objects databases by clustering and perturbation

Abul, Osman; Bonchi, Francesco; Nanni, Mirco

doi:10.1016/j.is.2010.05.003

Cited by 140 publications

(132 citation statements)

References 49 publications

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“…Consequently, several anonymity notions and methods for trajectories have been proposed [21,20,22,7,37,6,1,33,43,31,34,47,32,2,23,24]. Among those works, we next review the ones that are most similar to our approach, and we highlight our comparative advantages.…”

Section: Trajectory Anonymizationmentioning

confidence: 99%

“…Among those works, we next review the ones that are most similar to our approach, and we highlight our comparative advantages. Other comparisons of several trajectory anonymization methods can be found in [6,2].…”

Section: Trajectory Anonymizationmentioning

confidence: 99%

“…Closest to our approach is the notion of (k, δ)-anonymity [1,2]. In the original method -Never Walk Alone (NWA) [1]-, the set of trajectories is partitioned into disjoint subsets in which trajectories begin and end at roughly the same time; then trajectories within each set are clustered using the Euclidean distance.…”

Section: Trajectory Anonymizationmentioning

confidence: 99%

“…In the original method -Never Walk Alone (NWA) [1]-, the set of trajectories is partitioned into disjoint subsets in which trajectories begin and end at roughly the same time; then trajectories within each set are clustered using the Euclidean distance. In the follow-up method -Wait For Me (W4M) [2]-, the original trajectories are clustered using the edit distance on real sequences (EDR) [9]. Both approaches proceed by anonymizing each cluster separately.…”

Section: Trajectory Anonymizationmentioning

confidence: 99%

“…where p 1 and q 1 are the first elements of a given sequence, Rest(·) is a function that returns the input sequence without the first element, and where match(p, q) := 0 if p and q are "close", that is, they satisfy either |p.x − q.x| ≤ ϵ and |p.y − q.y| ≤ ϵ for some parameter ϵ [9] EDR has been employed for anonymization in [2]. However, the edit distance and variations thereof are not suitable to guide clustering for anonymization purposes.…”

Section: Trajectory Similarity Measuresmentioning

confidence: 99%

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Microaggregation- and permutation-based anonymization of movement data

Domingo-Ferrer

Trujillo-Rasúa

2012

Information Sciences

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Movement data, that is, trajectories of mobile objects, are automatically collected in huge quantities by technologies such as GPS, GSM or RFID, among others. Publishing and exploiting such data is essential to improve transportation, to understand the dynamics of the economy in a region, etc. However, there are obvious threats to the privacy of individuals if their trajectories are published in a way which allows re-identification of the individual behind a trajectory. We contribute to the literature on privacy-preserving publication of trajectories by presenting a distance measure for trajectories which naturally considers both spatial and temporal aspects of trajectories, is computable in polynomial time, and can cluster trajectories not defined over the same time span. Our distance measure can be naturally instantiated using other existing similarity measures for trajectories that are appropriate for anonymization purposes. Then, we propose two heuristics for trajectory anonymization which yield anonymized trajectories formed by fully accurate true original locations. The first heuristic is based on trajectory microaggregation using the above distance and on location permutation; it effectively achieves trajectory k-anonymity. The second heuristic is based only on location permutation; it gives up trajectory k-anonymity and aims at location k-diversity. The strong point of the second heuristic is that it takes into account reachability constraints when computing anonymized trajectories. Experimental results on a synthetic data set and a real-life data set are presented; for similar privacy protection levels and most reasonable parameter choices, our two methods offer better utility than comparable previous proposals in the literature.

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Section: Trajectory Anonymizationmentioning

confidence: 99%

Section: Trajectory Anonymizationmentioning

confidence: 99%

Section: Trajectory Anonymizationmentioning

confidence: 99%

Section: Trajectory Anonymizationmentioning

confidence: 99%

Section: Trajectory Similarity Measuresmentioning

confidence: 99%

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Microaggregation- and permutation-based anonymization of movement data

Domingo-Ferrer

Trujillo-Rasúa

2012

Information Sciences

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Semantic analysis in location privacy preserving

Xiong

Zhang

Zhu

2015

Concurrency and Computation

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SUMMARYWith the increasing use of location-based services, location privacy has recently started raising serious concerns. Location perturbation and obfuscation are most widely used for location privacy preserving. To protect a user from being identified, a cloaked spatial region that contains other k 1 nearest neighbors of the user is submitted to the location-based service provider, instead of the accurate position. In this paper, we consider the location-aware applications that services are different among regions. In such scenarios, the semantic distance between users should be considered besides the Euclidean distance for searching the neighbors of a user. We define a novel distance measurement that combines the semantic and the Euclidean distance to address the privacy-preserving issue in the aforementioned applications. We also present an algorithm kNNH to implement our proposed method. Moreover, we conduct performance study experiments on the proposed algorithm. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users' privacy. Copyright

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Practical Trajectory Anonymization Method Using Latent Space Generalization

Sakuma,

Nishi

2024

IEEJ Transactions Elec Engng

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The global positioning system (GPS) data are commonly used for location‐based services such as traffic flow prediction. However, such data contain considerable sensitive information and thus, they must be anonymized before being published. In this study, we investigate trajectory anonymization. Previous methods have limitations in that they cannot be applied for different load network sparseness and cannot preserve the trajectory information. Thus, we propose a DNN‐based method that can anonymize trajectories with different load network sparseness and also preserve the trajectory information. Specifically, the trajectories are projected to the latent space using the pre‐trained encoder‐decoder model, and the latent variables are generalized. Furthermore, to reduce the information loss, we propose a segment‐aware trajectory modeling and study the effectiveness of assuming the normal distribution to the latent space. The experimental results using real GPS data show the effectiveness of the proposed method, presenting the improvement in the data reservation rate by approximately 3% and reducing the reconstruction error by approximately 31%. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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Anonymization of moving objects databases by clustering and perturbation

Cited by 140 publications

References 49 publications

Microaggregation- and permutation-based anonymization of movement data

Microaggregation- and permutation-based anonymization of movement data

Semantic analysis in location privacy preserving

Practical Trajectory Anonymization Method Using Latent Space Generalization

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