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Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks

Published: 06 November 2024 Publication History

Abstract

As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the problem. A common concern in OSNs is how to form effective collaborative teams, as various tasks are completed through online collaborative networks. A team’s diversity in expertise has received high attention to producing high team performance in developing team formation (TF) algorithms. However, the effect of team diversity on performance under different types of tasks has not been extensively studied. Another important issue is how to balance the need to preserve individuals’ privacy with the need to maximize performance through active collaboration, as these two goals may conflict with each other. This research has not been actively studied in the literature. In this work, we develop a TF algorithm in the context of OSNs that can maximize team performance and preserve team members’ privacy under different types of tasks. Our proposed PRivAcy-Diversity-Aware TF framework, called PRADA-TF, is based on trust relationships between users in OSNs where trust is measured based on a user’s expertise and privacy preference levels. The PRADA-TF algorithm considers the team members’ domain expertise, privacy preferences, and the team’s expertise diversity in the process of TF. Our approach employs game-theoretic principles Mechanism Design to motivate self-interested individuals within a TF context, positioning the mechanism designer as the pivotal team leader responsible for assembling the team. We use two real-world datasets (i.e., Netscience and IMDb) to generate different semi-synthetic datasets for constructing trust networks using a belief model (i.e., Subjective Logic) and identifying trustworthy users as candidate team members. We evaluate the effectiveness of our proposed PRADA-TF scheme in four variants against three baseline methods in the literature. Our analysis focuses on three performance metrics for studying OSNs: social welfare, privacy loss, and team diversity.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 5
October 2024
719 pages
EISSN:2157-6912
DOI:10.1145/3613688
  • Editor:
  • Huan Liu
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2024
Online AM: 05 June 2024
Accepted: 02 April 2024
Revised: 29 March 2024
Received: 13 July 2023
Published in TIST Volume 15, Issue 5

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Author Tags

  1. Team formation
  2. online social networks
  3. privacy-preserving
  4. diversity
  5. trust

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