SPoFC: a framework for stream data aggregation with local differential privacy
Collecting and analysing customers' data plays an essential role in the more intense market competition. It is critical to perform data analysis effectively while ensuring the user's privacy, especially after various privacy regulations are enacted. In this paper, we consider the problem o...
Saved in:
Main Authors: | Yang, Mengmeng, Lam, Kwok-Yan, Zhu, Tianqing, Tang, Chenghua |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/168434 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Local differential privacy and its applications: a comprehensive survey
by: Yang, Mengmeng, et al.
Published: (2024) -
Secure hot path crowdsourcing with local differential privacy under fog computing architecture
by: Yang, Mengmeng, et al.
Published: (2021) -
Privacy enhanced matrix factorization for recommendation with local differential privacy
by: Shin, Hyejin, et al.
Published: (2018) -
Differentially private distributed frequency estimation
by: Yang, Mengmeng, et al.
Published: (2023) -
Local differential privacy-based federated learning for Internet of Things
by: Zhao, Yang, et al.
Published: (2021)