VTDP: Privately Sanitizing Fine-grained Vehicle Trajectory Data with Boosted Utility

Liu, Bingyu; Xie, Shangyu; Wang, Han; Yuan, Hong; Ban, Xuegang; Mohammady, Meisam

doi:10.1109/tdsc.2019.2960336

Cited by 6 publications

(8 citation statements)

References 40 publications

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“…The work of [7] provides an interesting modification compared to the works discussed above, in that it includes the assumption that adversaries may possess background knowledge. The authors implement differential privacy for vehicle location data, finding that their solution protects against re-identification and produces high output utility.…”

Section: Literature Review Resultsmentioning

confidence: 99%

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Rannenberg¹,

Pape²,

Tronnier³

et al. 2021

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The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data. For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.

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Section: Literature Review Resultsmentioning

confidence: 99%

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Rannenberg¹,

Pape²,

Tronnier³

et al. 2021

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“…A popular way [11,21,26,55,58,72,76,97,129] to quantify close data preservation is by using similarity measures, which output a value representing how different two trajectories are. For example, in mechanisms such that a one-to-one correspondence between the original and sanitized trajectories exists, we can use similarity measures to compute the average values between each pair.…”

Section: Utility Metricsmentioning

confidence: 99%

“…For example, frequent sequential pattern mining looks at the k most common subtrajectories in the original and sanitized database, either by seeing if they match over the databases [17,18,127] or by comparing the counts of such [11,17,114]. Similarly, count queries [17,18,76,128] can be utilized to check whether the number of visitors to locations is retained or not. Additionally, some metrics tackle the preservation of number of trajectories [76], most visited locations [78], hotspots [26], location popularity [114], flow density [11,114] and the start and end points distribution (trip error [114]).…”

Section: Utility Metricsmentioning

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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“…Dwork [18] first proposed the notion of differential privacy that provides rigorous privacy guarantee for statistical databases [19] against arbitrary background knowledge possessed by adversaries. Such privacy notion has been extended to sanitize and release data for different applications, such as classification [53], histograms [13], search logs [31], locations [47], trajectories [37], and data synthesis [8,35]. To our best knowledge, we first address the deficiency in differentially private video analysis.…”

Section: Related Workmentioning

confidence: 99%

VideoDP: A Flexible Platform for Video Analytics with Differential Privacy

Wang

Xie

Yuan

2020

Proceedings on Privacy Enhancing Technologies

Self Cite

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Massive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, especially against unknown background knowledge. In this paper, to our best knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that VideoDP can generate accurate results for video analytics.

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VTDP: Privately Sanitizing Fine-grained Vehicle Trajectory Data with Boosted Utility

Cited by 6 publications

References 40 publications

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

SoK: Differentially Private Publication of Trajectory Data

VideoDP: A Flexible Platform for Video Analytics with Differential Privacy

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