Machine learning for synthetic data generation: a review

Y Lu, M Shen, H Wang, X Wang, C van Rechem… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Location privacy-preserving mechanisms in location-based services: A comprehensive survey

H Jiang, J Li, P Zhao, F Zeng, Z **ao… - ACM Computing Surveys …, 2021‏ - dl.acm.org
Location-based services (LBSs) provide enhanced functionality and convenience of
ubiquitous computing, but they open up new vulnerabilities that can be utilized to violate the …

Trustworthy distributed ai systems: Robustness, privacy, and governance

W Wei, L Liu - ACM Computing Surveys, 2025‏ - dl.acm.org
Emerging Distributed AI systems are revolutionizing big data computing and data
processing capabilities with growing economic and societal impact. However, recent studies …

Sok: Privacy-preserving data synthesis

Y Hu, F Wu, Q Li, Y Long, GM Garrido… - … IEEE Symposium on …, 2024‏ - ieeexplore.ieee.org
As the prevalence of data analysis grows, safeguarding data privacy has become a
paramount concern. Consequently, there has been an upsurge in the development of …

Ldptrace: Locally differentially private trajectory synthesis

Y Du, Y Hu, Z Zhang, Z Fang, L Chen, B Zheng… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Trajectory data has the potential to greatly benefit a wide-range of real-world applications,
such as tracking the spread of the disease through people's movement patterns and …

{PrivTrace}: Differentially Private Trajectory Synthesis by Adaptive Markov Models

H Wang, Z Zhang, T Wang, S He, M Backes… - 32nd USENIX Security …, 2023‏ - usenix.org
Publishing trajectory data (individual's movement information) is very useful, but it also
raises privacy concerns. To handle the privacy concern, in this paper, we apply differential …

Synthesizing realistic trajectory data with differential privacy

X Sun, Q Ye, H Hu, Y Wang, K Huang… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Vehicle trajectory data is critical for traffic management and location-based services.
However, the released trajectories raise serious privacy concerns because they contain …

L-srr: Local differential privacy for location-based services with staircase randomized response

H Wang, H Hong, L **ong, Z Qin, Y Hong - Proceedings of the 2022 …, 2022‏ - dl.acm.org
Location-based services (LBS) have been significantly developed and widely deployed in
mobile devices. It is also well-known that LBS applications may result in severe privacy …

Generative models for synthetic urban mobility data: A systematic literature review

A Kapp, J Hansmeyer, H Mihaljević - ACM Computing Surveys, 2023‏ - dl.acm.org
Although highly valuable for a variety of applications, urban mobility data are rarely made
openly available, as it contains sensitive personal information. Synthetic data aims to solve …

{PrivGraph}: differentially private graph data publication by exploiting community information

Q Yuan, Z Zhang, L Du, M Chen, P Cheng… - 32nd USENIX Security …, 2023‏ - usenix.org
Graph data is used in a wide range of applications, while analyzing graph data without
protection is prone to privacy breach risks. To mitigate the privacy risks, we resort to the …