Personalized Privacy-Preserving Publication of Trajectory Data by Generalization and Distortion of Moving Points

Mahdavifar, Samaneh; Deldar, Fatemeh; Mahdikhani, Hassan

doi:10.1007/s10922-021-09617-5

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…This method uses location generalization and local differential privacy technology, which can not only protect trajectory privacy but also maintain the strong correlation between adjacent spatiotemporal nodes in the trajectory. Mahdavifar et al 18 proposed a trajectory data publishing scheme. Firstly, each trajectory is assigned a privacy level according to the privacy protection requirements of its moving objects, then all trajectories are divided into a set of clusters based on a greedy strategy, the trajectories of the clusters are anonymized, and the data is finally released.…”

Section: Related Workmentioning

confidence: 99%

A differential privacy trajectory data storage and publishing scheme based on radix tree

Tian

Zhu

2023

Concurrency and Computation

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SummaryWith the rapid development of information technology, location‐based services (LBSs) are becoming more and more popular, and at the same time, the users' privacy issues are becoming more and more serious. Therefore, this article proposes a differential private trajectory data storage and publishing scheme based on radix tree (TPRT). Firstly, the original trajectory is preprocessed by considering semantic information and simplified by dividing the stay area to reduce the risk of privacy leakage. Then, differential privacy technology is used to add Laplace noise to sensitive locations to achieve generalization. Finally, a radix tree structure satisfying differential privacy is defined, which alleviates the problem of large space consumption caused by tree height. To protect privacy, Laplace noise is added to the sensitive counts of the radix tree and limited by the Markov chain and privacy degree. Compared with several existing methods, our method has better performance and higher data availability.

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Section: Related Workmentioning

confidence: 99%

A differential privacy trajectory data storage and publishing scheme based on radix tree

Tian

Zhu

2023

Concurrency and Computation

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“…These technologies have many benefits such as processing and collecting large-scale datasets in order to identify hidden knowledge [189]. Although these technologies have many benefits, individual and group privacy issues of various kinds can emanate from data processing [190][191][192][193]. Hence, these technologies are adopting many privacy-preserving solutions to address this group privacy problem [194][195][196].…”

Section: Group Privacy Preservation In Different Computing Paradigmsmentioning

confidence: 99%

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

2022

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Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications is has been rapidly expanding across the globe, improving people’s lifestyles with data-driven services (i.e., recommendations, smart healthcare, etc.). The synergy between AI and big data has become imperative considering the drastic growth in personal data stemming from diverse sources (cloud computing, IoT, social networks, etc.). However, when data meet AI at some central place, it invites unimaginable privacy issues, and one of those issues is group privacy. Despite being the most significant problem, group privacy has not yet received the attention of the research community it is due. Problem Statement: We study how to preserve the privacy of particular groups (a community of people with some common attributes/properties) rather than an individual in personal data handling (i.e., sharing, aggregating, and/or performing analytics, etc.), especially when we talk about groups purposely made by two or more people (with clear group identifying markers), for whom we need to protect their privacy as a group. Aims/Objectives: With this technical letter, our aim is to introduce a new dimension of privacy (e.g., group privacy) from technical perspectives to the research community. The main objective is to advocate the possibility of group privacy breaches when big data meet AI in real-world scenarios. Methodology: We set a hypothesis that group privacy (extracting group-level information) is a genuine problem, and can likely occur when AI-based techniques meet high dimensional and large-scale datasets. To prove our hypothesis, we conducted a substantial number of experiments on two real-world benchmark datasets using AI techniques. Based on the experimental analysis, we found that the likelihood of privacy breaches occurring at the group level by using AI techniques is very high when data are sufficiently large. Apart from that, we tested the parameter effect of AI techniques and found that some parameters’ combinations can help to extract more and fine-grained data about groups. Findings: Based on experimental analysis, we found that vulnerability of group privacy can likely increase with the data size and capacity of the AI method. We found that some attributes of people can act as catalysts in compromising group privacy. We suggest that group privacy should also be given due attention as individual privacy is, and robust tools are imperative to restrict implications (i.e., biased decision making, denial of accommodation, hate speech, etc.) of group privacy. Significance of results: The obtained results are the first step towards responsible data science, and can pave the way to understanding the phenomenon of group privacy. Furthermore, the results contribute towards the protection of motives/goals/practices of minor communities in any society. Concluding statement: Due to the significant rise in digitation, privacy issues are mutating themselves. Hence, it is vital to quickly pinpoint emerging privacy threats and suggest practical remedies for them in order to mitigate their consequences on human beings.

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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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Personalized Privacy-Preserving Publication of Trajectory Data by Generalization and Distortion of Moving Points

Cited by 11 publications

References 43 publications

A differential privacy trajectory data storage and publishing scheme based on radix tree

A differential privacy trajectory data storage and publishing scheme based on radix tree

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

Practical Trajectory Anonymization Method Using Latent Space Generalization

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