Local differential privacy for human-centered computing

Local differential privacy for human-centered computing

Abstract Human-centered computing in cloud, edge, and fog is one of the most concerning issues. Edge and fog nodes generate huge amounts of data continuously, and the analysis of these data provides valuable information. But they also increase privacy risks. The personal sensitive data may be disclo...

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Bibliographic Details
Main Authors: Xianjin Fang, Qingkui Zeng, Gaoming Yang
Format: Article
Language:English
Published: SpringerOpen 2020-03-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
Human-centered computing
Local differential privacy
Laplace noise
Count sketch
Online Access:http://link.springer.com/article/10.1186/s13638-020-01675-8

Internet

http://link.springer.com/article/10.1186/s13638-020-01675-8

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