Break the Data Barriers While Keeping Privacy: A Graph Differential Privacy Method-Reference-Cited by-同舟云学术

Break the Data Barriers While Keeping Privacy: A Graph Differential Privacy Method

Published:2023-03-01 Issue:5 Volume:10 Page:3840-3850
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Li Yijing¹^ORCID,Tao Xiaofeng¹^ORCID,Zhang Xuefei¹^ORCID,Wang Mingsi²,Wang Shuo³^ORCID

Affiliation:

1. National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, China

2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

3. National Engineering Laboratory of Electric Vehicle and Collaborative Innovation Center for Electric Vehicle, Beijing Institute of Technology, Beijing, China

Funder

National Natural Science Foundation of China

111 Project of China

National Youth Top-Notch Talent Support Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10049212/09714260.pdf?arnumber=9714260

Reference38 articles.

1. Relational inductive biases, deep learning, and graph networks;battaglia;arXiv 1806 01261,2018

2. The Exponential Mechanism for Social Welfare: Private, Truthful, and Nearly Optimal

3. Big Data Analytics Architecture for Internet-of-Vehicles Based on the Spark

4. Achieving Differentially Private Location Privacy in Edge-Assistant Connected Vehicles

5. Improvement of the Recognition of Relationships in Social Networks Using Complementary Graph Coloring Based on Cellular Automata

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