research-article

Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation

Authors:

Bolin DingAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 3

Article No.: 60, Pages 1 - 26

https://doi.org/10.1145/3569452

Published: 07 February 2023 Publication History

Abstract

Recently, privacy issues in web services that rely on users’ personal data have raised great attention. Despite that recent regulations force companies to offer choices for each user to opt-in or opt-out of data disclosure, real-world applications usually only provide an “all or nothing” binary option for users to either disclose all their data or preserve all data with the cost of no personalized service.

In this article, we argue that such a binary mechanism is not optimal for both consumers and platforms. To study how different privacy mechanisms affect users’ decisions on information disclosure and how users’ decisions affect the platform’s revenue, we propose a privacy-aware recommendation framework that gives users fine control over their data. In this new framework, users can proactively control which data to disclose based on the tradeoff between anticipated privacy risks and potential utilities. Then we study the impact of different data disclosure mechanisms via simulation with reinforcement learning due to the high cost of real-world experiments. The results show that the platform mechanisms with finer split granularity and more unrestrained disclosure strategy can bring better results for both consumers and platforms than the “all or nothing” mechanism adopted by most real-world applications.

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Index Terms

Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 3

July 2023

890 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3582880

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 07 February 2023

Online AM: 25 October 2022

Accepted: 13 October 2022

Revised: 06 October 2022

Received: 27 May 2022

Published in TOIS Volume 41, Issue 3

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