A Survey of Privacy-Preserving Techniques on Trajectory Data

Li, Songyuan; Shen, Hong; Sang, Yingpeng

doi:10.1007/978-981-15-2767-8_41

Cited by 3 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Determined by its nature, the trajectory database has the characteristics of high dimensionality, sparseness, and sequentiality [13]. In recent years, privacy preserved data publication has become a research hotspot in the fields of network security and data security [14], [15]. Many researchers have accumulated a diversity of research results and manifested rich achievements in privacy protection level, data utility, publishing data characteristics and so on [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Risk-Aware Individual Trajectory Data Publishing With Differential Privacy

et al. 2021

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Risk-Aware Individual Trajectory Data Publishing With Differential Privacy

et al. 2021

View full text Add to dashboard Cite

Security Analysis of Trajectory Privacy Protection Methods in Crowdsensing Networks

王

2023

SEA

View full text Add to dashboard Cite

Personalized Privacy Preservation in Consumer Mobile Trajectories

Macha

Foutz

et al. 2024

Information Systems Research

View full text Add to dashboard Cite

The use of mobile technologies to collect and analyze consumer location data has created a multi-billion-dollar ecosystem with various stakeholders. However, this ecosystem also presents privacy risks to consumers. To address this issue, data aggregators can implement a privacy preserving framework that balances privacy risks to consumers with data utilities for advertisers. The proposed framework is personalized and flexible, allowing for quantification of personalized privacy risks and data obfuscation to reduce these risks. It can accommodate a variety of risks, utilities, and trade-offs between the two. The framework was validated on one million consumer location trajectories, revealing potential privacy risks in the absence of data obfuscation. Machine learning methods are used to demonstrate the effectiveness of the proposed framework which outperformed ten baselines from the latest literature, significantly reducing each consumer’s privacy risk while preserving advertiser utility. As the use of location big data continues to grow, this research offers a necessary framework to balance privacy risks and data utilities, sustain a secure and self-governing ecosystem, and ensure the protection of consumers’ personal data.

show abstract

A Survey of Privacy-Preserving Techniques on Trajectory Data

Cited by 3 publications

References 32 publications

Risk-Aware Individual Trajectory Data Publishing With Differential Privacy

Risk-Aware Individual Trajectory Data Publishing With Differential Privacy

Security Analysis of Trajectory Privacy Protection Methods in Crowdsensing Networks

Personalized Privacy Preservation in Consumer Mobile Trajectories

Contact Info

Product

Resources

About