Optimal Trading Mechanism Based on Differential Privacy Protection and Stackelberg Game in Big Data Market-Reference-Cited by-同舟云学术

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Optimal Trading Mechanism Based on Differential Privacy Protection and Stackelberg Game in Big Data Market

Published:2023-09 Issue:5 Volume:16 Page:3550-3563
ISSN:1939-1374
Container-title:IEEE Transactions on Services Computing
language:
Short-container-title:IEEE Trans. Serv. Comput.

Author:

Li Chuang¹^ORCID,He Aoli¹^ORCID,Wen Yanhua¹^ORCID,Liu Gang²^ORCID,Chronopoulos Anthony Theodore³^ORCID

Affiliation:

1. College of Computer Science, Hunan University of Technology and Business, Changsha, Hunan, China

2. College of Information Science and Engineering, Hunan University, Changsha, Hunan, China

3. Department of Computer Science, University of Texas, San Antonio, TX, USA

Funder

Young Program of National Natural Science Foundation of China

Key Research and Development Plan of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/4629386/10274114/10037216.pdf?arnumber=10037216

Reference40 articles.

1. Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs

2. A pricing model for Big Personal Data

3. Optimal Pricing Mechanism for Data Market in Blockchain-Enhanced Internet of Things

4. A Mechanism Design Approach to Resource Procurement in Cloud Computing

5. Private Memoirs of IoT Devices: Safeguarding User Privacy in the IoT Era

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1. Get by how much you pay: A novel data pricing scheme for data trading;Information Processing & Management;2024-11

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3. Pricing Game and Blockchain for Electricity Data Trading in Low-Carbon Smart Energy Systems;IEEE Transactions on Industrial Informatics;2024-04

4. RFL-APIA: A Comprehensive Framework for Mitigating Poisoning Attacks and Promoting Model Aggregation in IIoT Federated Learning;IEEE Transactions on Industrial Informatics;2024

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