Differential Private POI Queries via Johnson-Lindenstrauss Transform
The growing popularity of location-based services is giving untrusted servers relatively free reign to collect huge amounts of location information from mobile users. This information can reveal far more than just a user’s locations but other sensitive information, such as the user&#x...
Saved in:
Main Authors: | Mengmeng Yang, Tianqing Zhu, Bo Liu, Yang Xiang, Wanlei Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2018-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8368163/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Framework for Privacy-Preserving in IoV Using Federated Learning With Differential Privacy
by: Muhammad Adnan, et al.
Published: (2025-01-01) -
A Framework for Tradeoff Between Location Privacy Preservation and Quality of Experience in Location Based Services
by: Tianyi Feng, et al.
Published: (2024-01-01) -
Privacy Auditing in Differential Private Machine Learning: The Current Trends
by: Ivars Namatevs, et al.
Published: (2025-01-01) -
A privacy budget adaptive optimization scheme for federated computing power Internet of things
by: MA Wenyu, et al.
Published: (2024-12-01) -
K-Means Clustering with Local Distance Privacy
by: Mengmeng Yang, et al.
Published: (2023-12-01)