SoSharP: Recommending Sharing Policies in Multiuser Privacy Scenarios

Fogues, Ricard L.; Murukannaiah, Pradeep K.; Such, José M.; Singh, Munindar P.

doi:10.1109/mic.2017.4180836

Cited by 20 publications

(6 citation statements)

References 14 publications

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“…This included studies based on secret sharing [16], the Clarke-Tax mechanism to encourage uploaders to acknowledge co-owners [130,131], aggregated voting [23,118,141], a new framework to reach a consensus based on co-owners' trust values [6], novel access control models [56-59, 145, 150], computational mechanisms for adaptive audience recommendation [135,136], and AI-based negotiation techniques that use game theory [115,139], argumentation theory [36,38,72,94] and human values theory [92][93][94]. Furthermore, Fogues et al [36,37] implemented a recommendation system trained with data collected from a user survey. Keküllüoğlu et al [69,70] and Rajtmajer et al [115] considered multiple interactions between OSN users over time.…”

Section: Precautionary Mechanisms: Novel Solutions To Manage Mpcsmentioning

confidence: 99%

When Forcing Collaboration is the Most Sensible Choice

Cherubini

Niksirat

Boldi

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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Individuals share increasing amounts of personal multimedia data, exposing themselves (uploaders) as well as others (data subjects). Non-consensual sharing of multimedia data that depicts others raises so-called multiparty privacy conflicts (MPCs), which can have severe consequences. To limit the incidence of MPCs, a family of Precautionary mechanisms have recently been developed that force uploaders to collaborate with the other data subjects to prevent MPCs. However, there is still very little work on understanding how users perceive the precautionary mechanisms together with which ones they prefer and why. In addition, precautionary mechanisms have some limitations, e.g., they require linking content to the co-owners' identity. Therefore, we also explore alternatives to precautionary mechanisms and propose a new class of solutions-Dissuasive mechanisms-that aim at deterring the uploaders from sharing without consent. We then present a user-centric comparison of precautionary and dissuasive mechanisms, through a large-scale survey ( = 1792; a representative sample of adult Internet users). Our results showed that respondents prefer precautionary to dissuasive mechanisms. These enforce collaboration, provide more control to the data subjects, but also they reduce uploaders' uncertainty around what is considered appropriate for sharing. We learned that threatening legal consequences is the most desirable dissuasive mechanism, and that respondents prefer the mechanisms that threaten users with immediate consequences (compared with delayed consequences). Dissuasive mechanisms are in fact well received by frequent sharers and older users, while precautionary mechanisms are preferred by women and younger users. We discuss the implications for design, including considerations about side leakages, consent collection, and censorship. CCS Concepts: • Security and privacy → Usability in security and privacy; • Human-centered computing;

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Section: Precautionary Mechanisms: Novel Solutions To Manage Mpcsmentioning

confidence: 99%

When Forcing Collaboration is the Most Sensible Choice

Cherubini

Niksirat

Boldi

et al. 2021

Proc. ACM Hum.-Comput. Interact.

Self Cite

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“…The use of machine learning for privacy is gaining momentum and the research area is still open for further improvement. Fogues et al [8] provide an agent-based approach which requires user input when required to learn incrementally about user policies, and recommends privacy policies for sharing content for multiuser scenarios. Vanetti et al [25] propose a machine learning approach for filtering unwanted textual contents in OSNs.…”

Section: Discussionmentioning

confidence: 99%

Agents for Preserving Privacy: Learning and Decision Making Collaboratively

Ulusoy

Yolum

2020

Multi-Agent Systems and Agreement Technologies

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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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“…The second family of solutions hides the content from undesired audiences, for instance, hiding family related content from friends in the colleague circle. To determine the 'undesired audiences', earlier studies relied on computational approaches to automate privacy decisions such as novel access-control models [49-52, 123, 129], recommender systems [35,36], adaptive audience recommendations [114,115], aggregated voting [19,104,119], a collaborative access-control system based on secret sharing [12], trust-based consent collection [2], and mechanisms for supporting co-owners' interactions in negotiating privacy settings [58,59,101]. Some of these studies were built upon theories such as the use of game theory for negotiation [101,118], argumentation theory [35,37,63,88,90], and human-values theory [88][89][90].…”

Section: Protective Mechanisms For Osnsmentioning

confidence: 99%

“I thought you were okay”: Participatory Design with Young Adults to Fight Multiparty Privacy Conflicts in Online Social Networks

Niksirat

Anthoine-Milhomme

Randin

et al. 2021

Designing Interactive Systems Conference 2021

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Although sharing multimedia content on online social networks (OSNs) has many benefits, publishing photos or videos of other people-without obtaining permission-can cause multiparty privacy conflicts (MPCs). Early studies developed technical solutions and dissuasive approaches to address MPCs. However, none of these studies involved, in the design process, the OSN users who have experienced MPCs. Hence, they possibly overlooked the valuable experiences these individuals have accrued. To fill this gap, we recruited participants specifically from this population of users, and we involved them in participatory design sessions to find solutions to reduce the incidence of MPCs. To frame the activities of our participants, we borrowed terminology and concepts from a well-known framework used in the justice systems. Over the course of several design sessions, our participants designed 10 solutions to mitigate MPCs. The designed solutions are based on different mechanisms, including preventing MPCs from occurring, dissuading users from sharing, resolving the conflicts, and educating users about community standards. We discuss the open design and research opportunities suggested by the designed solutions and contribute an ideal workflow that synthesizes the best of each solution. We contribute to the innovation of privacy-enhancing technologies to limit the incidence of MPCs in OSNs. CCS CONCEPTS• Security and privacy → Usability in security and privacy; • Human-centered computing;

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SoSharP: Recommending Sharing Policies in Multiuser Privacy Scenarios

Cited by 20 publications

References 14 publications

When Forcing Collaboration is the Most Sensible Choice

When Forcing Collaboration is the Most Sensible Choice

Agents for Preserving Privacy: Learning and Decision Making Collaboratively

“I thought you were okay”: Participatory Design with Young Adults to Fight Multiparty Privacy Conflicts in Online Social Networks

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