203,981 research outputs found

    Resolving Multi-party Privacy Conflicts in Social Media

    Get PDF
    Items shared through Social Media may affect more than one user's privacy --- e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. 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 paper, 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 modelling 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' behaviour.Comment: Authors' version of the paper accepted for publication at IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Knowledge and Data Engineering, 201

    A Privacy-Preserving Framework Using Hyperledger Fabric for EHR Sharing Applications

    Get PDF
    Electronic Health Records, or EHRs, include private and sensitive information of a patient. The privacy of personal healthcare data can be protected through Hyperledger Fabric, a permissioned blockchain framework. A few Hyperledger Fabric- integrated EHR solutions have emerged in recent years. However, none of them implements the privacy-preserving techniques of Hyperledger Fabric to make transactions anonymous or preserve the transaction data privacy during the consensus. Our proposed architecture is built on Hyperledger Fabric and its privacy-preserving mechanisms, such as Identity Mixer, Private Data Collections, Channels and Transient Fields to securely store and transfer patient-sensitive data while providing anonymity and unlinkability of transactions

    Privacy-preserving Transactions on the Web

    Get PDF
    There is a rapid growth in the number of applications using sensitive and personal information on the World Wide Web. This growth creates an urgent need to maintain the anonymity of the participants in many web transactions and to preserve the privacy of their sensitive data during data dissemination over the web. First, maintaining the anonymity of users on the World Wide Web is essential for a number of web applications. Anonymity cannot be assured by single interested individuals or an organization but requires participation from other web nodes owned by other entities. Second, preserving the privacy of sensitive data is another very important issue in web transactions. Today, exchanging and sharing personal data between various participants in web transactions endangers privacy. In this article, we discuss various research directions and challenges that need to be addressed while trying to accomplish our goal of maintaining the anonymity of participants and preserving the privacy of sensitive data in web transactions. To maintain anonymity of participants in a web transaction, we propose a method based on the modi fied form of the club mechanism with economic incentives, a solution which rests upon the Prisoner’s Dilemma approach. We compare our approach to other well-known dat a-sharing approaches such as Crowds, Tor, Tarzan and LPWA. To maintain the privacy of sensitive data, we propose a solution based on privacy-preserving data dissemination (P2D2). We also present a solution to implement our approach using Semantic Web Rule Languages and Jena—a Java-based inference engine
    • …
    corecore