With the development of Internet-of-Things (IoT) technology, various types of information, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance, and other forms of the IoT information service model gradually change people’s daily lives. Facing the vast amounts of IoT information data, the IoT search technology is used to quickly find accurate information to meet the real-time search needs of users. However, IoT search requires using a large amount of user private information, such as personal health information, location information, and social relations information, to provide personalized services. Employing private information from users will encounter security problems if an effective access control mechanism is missing during the IoT search process. An access control mechanism can effectively monitor the access activities of resources and ensure that authorized users access information resources under legitimate conditions. This survey examines the growing literature on access control for an IoT search. Problems and challenges of access control mechanisms are analyzed to facilitate the adoption of access control solutions in real-life settings. This article aims to provide theoretical, methodological, and technical guidance for IoT search access control mechanisms in large-scale dynamic heterogeneous environments. Based on a literature review, we also analyzed the future development direction of access control in the age of IoT.

A Survey on Access Control in the Age of Internet of Things

Items in social media such as photos may be co-owned by multiple users, i.e., the sharing decisions of the ones who upload them have the potential to harm the privacy of the others. Previous works uncovered coping strategies by co-owners to manage their privacy, but mainly focused on general practices and experiences. We establish an empirical base for the prevalence, context and severity of privacy conflicts over co-owned photos. To this aim, a parallel survey of pre-screened 496 uploaders and 537 co-owners collected occurrences and type of conflicts over co-owned photos, and any actions taken towards resolving them. We uncover nuances and complexities not known before, including co-ownership types, and divergences in the assessment of photo audiences. We also find that an all-or-nothing approach seems to dominate conflict resolution, even when parties actually interact and talk about the conflict. Finally, we derive key insights for designing systems to mitigate these divergences and facilitate consensus.

https://dl.acm.org/doi/pdf/10.1145/3025453.3025668

Photo Privacy Conflicts in Social Media: A Large-scale Empirical Study

The last decades have seen a growing interest and demand for community-centered collaborative systems and platforms. These systems and platforms aim to provide an environment in which users can collaboratively create, share, and manage resources. While offering attractive opportunities for online collaboration and information sharing, they also open several security and privacy issues. This has attracted several research efforts toward the design and implementation of novel access control solutions that can handle the complexity introduced by collaboration. Despite these efforts, transition to practice has been hindered by the lack of maturity of the proposed solutions. The access control mechanisms typically adopted by commercial collaborative systems like online social network websites and collaborative editing platforms, are still rather rudimentary and do not provide users with a sufficient control over their resources. This survey examines the growing literature on access control for collaborative systems centered on communities, and identifies the main challenges to be addressed in order to facilitate the adoption of collaborative access control solutions in real-life settings. Based on the literature study, we delineate a roadmap for future research in the area of access control for community-centered collaborative systems.

/pdf/survey-on-access-control-for-community-centered-1mmqtz03zy.pdf

Survey on Access Control for Community-Centered Collaborative Systems

The privacy of individuals does not only depend on their own actions and data but may also be affected by the privacy decisions and by the data shared by other individuals. This interdependence is an essential aspect of privacy and ignoring it can lead to serious privacy violations. In this survey, we summarize and analyze research on interdependent privacy risks and on the associated (cooperative and non-cooperative) solutions. We also demonstrate that interdependent privacy has been studied in isolation in different research communities. By doing so, we systematize knowledge on interdependent privacy research and provide insights on how this research should be conducted and which challenges it should address.

/pdf/a-survey-on-interdependent-privacy-2wp6tdyda2.pdf

A Survey on Interdependent Privacy

Social network services (SNSs) enable users to conveniently share personal information. Often, the information shared concerns other people, especially other members of the SNS. In such situations, two or more people can have conflicting privacy preferences; thus, an appropriate sharing policy may not be apparent. We identify such situations as multiuser privacy scenarios. Current approaches propose finding a sharing policy through preference aggregation. However, studies suggest that users feel more confident in their decisions regarding sharing when they know the reasons behind each other’s preferences. The goals of this paper are (1) understanding how people decide the appropriate sharing policy in multiuser scenarios where arguments are employed, and (2) developing a computational model to predict an appropriate sharing policy for a given scenario. We report on a study that involved a survey of 988 Amazon Mechanical Turk (MTurk) users about a variety of multiuser scenarios and the optimal sharing policy for each scenario. Our evaluation of the participants’ responses reveals that contextual factors, user preferences, and arguments influence the optimal sharing policy in a multiuser scenario. We develop and evaluate an inference model that predicts the optimal sharing policy given the three types of features. We analyze the predictions of our inference model to uncover potential scenario types that lead to incorrect predictions, and to enhance our understanding of when multiuser scenarios are more or less prone to dispute.

/pdf/sharing-policies-in-multiuser-privacy-scenarios-3z6cjswl4r.pdf

Sharing Policies in Multiuser Privacy Scenarios: Incorporating Context, Preferences, and Arguments in Decision Making

Information shared through Social Media may affect more than one user’s privacy e.g., Information that depicts different users, comments that mention different users, events in which different users are invited, etc. Many types of privacy management support in present mainstream Social Media foundation makes users unable to appropriately control the sender and receiver. Computational mechanisms that are able to merge the privacy preferences of different users into a single policy for an item can help solve this problem. Merging different user’s personal preferences is difficult hence conflicts occur in privacy preferences, so methods to resolve conflicts are needed. Moreover, these techniques need to consider how users’ would actually reach an engagement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the information to be shared. present approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. Here, we introduce the basic computational procedure to overcome problems in Social Media that is able to adapt to different situations by modeling the concessions that users make to reach answers to the conflicts. The present results of a user study in which our introduced mechanism outperformed other present approaches in terms of how many times each approach matched users’ action. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users’ privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users’ would actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. In this project, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modeling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users’ behavior.

/pdf/resolving-multi-party-privacy-conflicts-in-social-media-32ca6vy5y5.pdf

Selvaraj

Papers

Resolving Multi-party Privacy Conflicts in Social Media