K-means clustering with local dᵪ-privacy for privacy-preserving data analysis
Privacy-preserving data analysis is an emerging area that addresses the dilemma of performing data analysis on user data while protecting users' privacy. In this paper, we consider the problem of constructing privacy-preserving K -means clustering protocol for data analysis that provides local...
Saved in:
Main Authors: | Yang, Mengmeng, Tjuawinata, Ivan, Lam, Kwok-Yan |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/168038 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Secure hot path crowdsourcing with local differential privacy under fog computing architecture
by: Yang, Mengmeng, et al.
Published: (2021) -
PRIVACY PRESERVING METHODS FOR LOCALIZATION
by: FENG TIANYI
Published: (2021) -
Local differential privacy and its applications: a comprehensive survey
by: Yang, Mengmeng, et al.
Published: (2024) -
SPoFC: a framework for stream data aggregation with local differential privacy
by: Yang, Mengmeng, et al.
Published: (2023) -
Local differential privacy-based federated learning for Internet of Things
by: Zhao, Yang, et al.
Published: (2021)