Literature Database Entry

tao2023distributed

Youming Tao, Shuzhen Chen, Feng Li, Dongxiao Yu, Jiguo Yu and Hao Sheng, "A Distributed Privacy-Preserving Learning Dynamics in General Social Networks," IEEE Transactions on Knowledge and Data Engineering, vol. 35 (9), pp. 9547–9561, September 2023.

Abstract

In this article, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm.

Quick access

Original Version DOI (at publishers web site)
BibTeX BibTeX

Contact

Youming Tao
Shuzhen Chen
Feng Li
Dongxiao Yu
Jiguo Yu
Hao Sheng

BibTeX reference

@article{tao2023distributed,
    author = {Tao, Youming and Chen, Shuzhen and Li, Feng and Yu, Dongxiao and Yu, Jiguo and Sheng, Hao},
    doi = {10.1109/tkde.2023.3241442},
    title = {{A Distributed Privacy-Preserving Learning Dynamics in General Social Networks}},
    pages = {9547--9561},
    journal = {IEEE Transactions on Knowledge and Data Engineering},
    issn = {1041-4347},
    publisher = {IEEE},
    month = {9},
    number = {9},
    volume = {35},
    year = {2023},
   }
   
   

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.

Last modified: 2024-05-20