@inproceedings{09d73f38b6af49769611c1cd8c303d33,
title = "Agents for Preserving Privacy: Learning and Decision Making Collaboratively",
abstract = "Privacy is a right of individuals to keep personal information to themselves. Often online systems enable their users to select what information they would like to share with others and what information to keep private. When an information pertains only to a single individual, it is possible to preserve privacy by providing the right access options to the user. However, when an 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 as individuals might have conflicting privacy constraints. Resolving this problem requires an automated mechanism that takes into account the relevant individuals{\textquoteright} concerns to decide on the privacy configuration of information. Accordingly, this paper proposes an auction-based privacy mechanism to manage the privacy of users when information related to multiple individuals are at stake. We propose to have a software agent that acts on behalf of each user to enter privacy auctions, learn the subjective privacy valuations of the individuals over time, and to bid to respect their privacy. We show the workings of our proposed approach over multiagent simulations.",
keywords = "Multiagent systems, Online social networks, Privacy",
author = "Onuralp Ulusoy and Pinar Yolum",
year = "2020",
doi = "10.1007/978-3-030-66412-1\_8",
language = "English",
isbn = "978-3-030-66411-4",
series = "Lecture Notes in Computer Science ",
publisher = "Springer",
pages = "116--131",
editor = "Bassiliades, \{Nick \} and Chalkiadakis, \{Georgios \} and Jonge, \{Dave de \}",
booktitle = "Multi-Agent Systems and Agreement Technologies",
edition = "1",
}