Hermes: A Privacy-Preserving Approximate Search Framework for Big Data

Hermes: A Privacy-Preserving Approximate Search Framework for Big Data

We propose a sampling-based framework for privacy-preserving approximate data search in the context of big data. The framework is designed to bridge multi-target query needs from users and the data platform, including required query accuracy, timeliness, and query privacy constraints. A novel privac...

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Bibliographic Details
Main Authors: Zhigang Zhou, Hongli Zhang, Shang Li, Xiaojiang Du
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Big data
bootstrapping
Hadoop
metrics
privacy-preserving
sampling
Online Access:https://ieeexplore.ieee.org/document/8241765/

Internet

https://ieeexplore.ieee.org/document/8241765/

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