Privacy in trajectory micro-data publishing : a survey

Fiore, Marco; Katsikouli, Panagiota; Zavou, Elli; Cunche, Mathieu; Fessant, Françoise; Hello, Dominique Le; Aivodji, Ulrich Matchi; Olivier, Baptiste; Quertier, Tony; Stanica, Razvan

doi:10.48550/arxiv.1903.12211

Cited by 3 publications

(5 citation statements)

References 79 publications

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“…3 https://www.cprd.com/content/synthetic-data#CPRD%20cardiovascular%20disease%20synthetic%20dataset. 4 https://www.cprd.com/content/synthetic-data#CPRD%20COVID-19%20symptoms%20and%20risk%20factors%20synthetic%20dataset. 5 For access to code and details about the R package bnlearn: https://cran.r-project.org/web/packages/bnlearn/index.html.…”

Section: Generative Modelsmentioning

confidence: 99%

“…Despite several safeguards that are put into place to ensure data privacy, linked datasets hold the additional risk of a potential “linkage attack” which refers to the attempt of an adversary to re-identify individuals in the linked data by using side information owned by the adversary. This information could be obtained by directly observing target individuals, mining suitable open data or gaining access to the original data through data breaches [ 4 ]. Hence, in the UK and elsewhere, measures to protect the confidentiality of data fall under the “Five safes” framework: safe data, safe people, safe projects, safe outputs, and safe settings [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An overview on synthetic administrative data for research

Kokosi

Stavola

Mitra

et al. 2022

IJPDS

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Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclosure risk can lead to lengthy approval processes and restricted data access. This can delay or prevent the production of timely evidence. A promising solution to facilitate more efficient data access is to create synthetic versions of the original datasets which do not hold any confidential information and can minimise disclosure risk. Such data may be used as an interim solution, allowing researchers to develop their analysis plans on non-disclosive data, whilst waiting for access to the real data. We aim to provide an overview of the background and uses of synthetic data, describe common methods used to generate synthetic data in the context of UK administrative research, propose a simplified terminology for categories of synthetic data, and illustrate challenges and future directions for research.

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Section: Generative Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An overview on synthetic administrative data for research

Kokosi

Stavola

Mitra

et al. 2022

IJPDS

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show abstract

“…While other surveys on trajectory privacy have been published already, they are either vertical (e.g., focusing on wireless sensor networks [21], opportunistic mobile networks [22], and automotive applications [23]), or lacking of a systematic categorization and evaluation of utility and privacy metrics (e.g., [24], [25], [26]), or have been published before well-known recent results (e.g., [27]). Overall, the main contributions of this paper are as follows:…”

Section: Positioning and Contributionsmentioning

confidence: 99%

“…Intuitively, the access to a trajectory dataset should not reveal too much additional information to what is already known by the adversary. Probabilistic attacks can be considered as a generalization of attribute linkage [26], since their goal is not to infer a specific sensitive attribute, but rather to increase the generic knowledge of an adversary. For instance, given some locations known by an adversary, while linkage attacks focus on specific sensitive data, a successful probabilistic attack can reveal the entire trajectory of an individual (as in record linkage) as well as the sensitive attributes related to that trajectory (as in attribute linkage).…”

Section: Probabilistic Modelsmentioning

confidence: 99%

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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“…Although a plethora of different anonymization techniques have been proposed for location data (for a very thorough survey see [19]), they all suffer from either weak utility, or weak privacy guarantees, or they are not scalable to large datasets. Indeed, location data are inherently high-dimensional and often sparse, which makes an individual's location trajectory unique even in very large populations 3 .…”

Section: Introductionmentioning

confidence: 99%

In Search of Lost Utility: Private Location Data

Lestyán

Ács²,

Biczók³

2022

PoPETs

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The unavailability of training data is a permanent source of much frustration in research, especially when it is due to privacy concerns. This is particularly true for location data since previous techniques all suffer from the inherent sparseness and high dimensionality of location trajectories which render most techniques impractical, resulting in unrealistic traces and non-scalable methods. Moreover, time information of location visits is usually dropped, or its resolution is drastically reduced. In this paper we present a novel technique for privately releasing a composite generative model and whole high-dimensional location datasets with detailed time information. To generate high-fidelity synthetic data, we leverage several peculiarities of vehicular mobility such as its language-like characteristics (“you should know a location by the company it keeps”) or how humans plan their trips from one point to the other. We model the generator distribution of the dataset by first constructing a variational autoencoder to generate the source and destination locations, and the corresponding timing of trajectories. Next, we compute transition probabilities between locations with a feed forward network, and build a transition graph from the output of this model, which approximates the distribution of all paths between the source and destination (at a given time). Finally, a path is sampled from this distribution with a Markov Chain Monte Carlo method. The generated synthetic dataset is highly realistic, scalable, provides good utility and, nonetheless, provably private. We evaluate our model against two state-of-theart methods and three real-life datasets demonstrating the benefits of our approach.

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Privacy in trajectory micro-data publishing : a survey

Cited by 3 publications

References 79 publications

An overview on synthetic administrative data for research

An overview on synthetic administrative data for research

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

In Search of Lost Utility: Private Location Data

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