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PANOLA: A Personal Assistant for Supporting Users in Preserving Privacy

Published:14 September 2021Publication History
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Abstract

Privacy is the right of individuals to keep personal information to themselves. When individuals use online systems, they should be given the right to decide what information they would like to share and what to keep private. When a piece of information pertains only to a single individual, preserving privacy is possible by providing the right access options to the user. However, when a piece of information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging. The problem becomes more difficult when the individuals who are affected by the information have different, possibly conflicting privacy constraints. Resolving this problem requires a mechanism that takes into account the relevant individuals’ concerns to decide on the privacy configuration of information. Because these decisions need to be made frequently (i.e., per each piece of shared content), the mechanism should be automated. This article presents a personal assistant to help end-users with managing the privacy of their content. When some content that belongs to multiple users is about to be shared, the personal assistants of the users employ an auction-based privacy mechanism to regulate the privacy of the content. To do so, each personal assistant learns the preferences of its user over time and produces bids accordingly. Our proposed personal assistant is capable of assisting users with different personas and thus ensures that people benefit from it as they need it. Our evaluations over multiagent simulations with online social network content show that our proposed personal assistant enables privacy-respecting content sharing.

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    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 22, Issue 1
      February 2022
      717 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3483347
      • Editor:
      • Ling Liu
      Issue’s Table of Contents

      Copyright © 2021 Association for Computing Machinery.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 14 September 2021
      • Accepted: 1 June 2021
      • Revised: 1 May 2021
      • Received: 1 July 2020
      Published in toit Volume 22, Issue 1

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