A Novel Data Obfuscation Framework Integrating Probability Density and Information Entropy for Privacy Preservation

A Novel Data Obfuscation Framework Integrating Probability Density and Information Entropy for Privacy Preservation

Data privacy protection is increasingly critical in fields like healthcare and finance, yet existing methods, such as Fully Homomorphic Encryption (FHE), differential privacy (DP), and federated learning (FL), face limitations like high computational complexity, noise interference, and communication...

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Bibliographic Details
Main Authors: Haolan Cheng, Chenyi Qiang, Lin Cong, Jingze Xiao, Shiya Liu, Xingyu Zhou, Huijun Wang, Mingzhuo Ruan, Chunli Lv
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
privacy protection and data security
probability density modeling
data obfuscation strategy
multi-objective optimization
Online Access:https://www.mdpi.com/2076-3417/15/3/1261
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