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.
Online social networks enable users to share content with other users. Many times, a shared content, such as a group picture, may reveal private information about the uploader as well as others who are associated with the content. Ideally, protection of privacy in such cases would need to consider the privacy concerns of all relevant individuals. However, these concerns might conflict and satisfying one user's privacy needs could cause a privacy violation for others. This calls for computational mechanisms that can decide on the privacy policies of the content collaboratively. Accordingly, we propose an agent-based collaborative privacy management model for online social networks (OSNs). Agents represent OSN users and manage their privacy requirements on their behalf. We extend Clarke-Tax mechanism for auctioning to achieve fair handling of privacy settings and to tax the agents whose privacy settings are chosen. We evaluate our approach over multi-agent simulations and show that it produces privacy policies efficiently and more accurately than existing approaches.
Collaborative systems, such as online social networks or Internet of Things, host vast amounts of content that is created and manipulated by multiple users. Co-edited documents or group pictures are prime examples of such co-owned content. Respecting privacy of users in collaborative systems is difficult because the co-owners of the shared content can have conflicting access policies about the content. To address this problem, recent approaches employ group decision making techniques, such as auctions. With these approaches, when a content is to be shared, all co-owners express their privacy preferences through the mechanism (e.g., by bidding) and the group decision mechanism reaches a decision to enable or deny access to the content. However, such mechanisms have to be carried out per content, making them impractical for most realistic settings. We argue that rather than employing a group decision mechanism on each content separately, it is more practical to watch for privacy norms that emerge in systems and make decisions using these norms, when possible. This paper borrows ideas from philosophy to represent privacy norms and develops algorithms to compute them in collaborative systems. We show that when privacy norms are identified correctly, they can enable collaborative systems respect users' privacy as well as decrease the need to engage in a group decision mechanism considerably. CCS CONCEPTS• Security and privacy → Privacy protections.
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' 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.
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