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Ontology-Enabled Access Control and Privacy Recommendations

机译:支持本体的访问控制和隐私建议

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Recent trends in ubiquitous computing target to provide user-controlled servers, providing a single point of access for managing different personal data in different Online Social Networks (OSNs), i.e. profile data and resources from various social interaction services (e.g., LinkedIn, Facebook, etc.). Ideally, personal data should remain independent of the environment, e.g., in order to support flexible migration to new landscapes. Such information interoperability can be achieved by ontology-based information representation and management. In this paper we present achievements and experiences of the di.me project, with respect to access control and privacy preservation in such systems. Special focus is put on privacy issues related to linkability and unwanted information disclosure. These issues could arise for instance when collecting and integrating information of different social contacts and their live streams (e.g., activity status, live posts, etc.). Our approach provides privacy recommendations by leveraging (1) the detection of semantic equivalence between contacts as portrayed in online profiles and (2) NLP techniques for analysing shared live streams. The final results after 3 years are presented and the portability to other environments is shortly discussed.
机译:近期计算目标的最近趋势提供用户控制的服务器,提供用于管理不同在线社交网络(OSN)的不同个人数据的单一访问点,即来自各种社交交互服务的简档数据和资源(例如,LinkedIn,Facebook,等等。)。理想情况下,个人数据应保持独立于环境,例如,以支持灵活的迁移到新的景观。这种信息互操作性可以通过基于本体的信息表示和管理来实现。在本文中,我们在此类系统中提供了访问控制和隐私保存的DI.ME项目的成就和经验。特别焦点符合与胶合性和不需要的信息披露相关的隐私问题。例如,在收集和整合不同社会联系人及其直播流的信息时,可能会出现这些问题(例如,活动状态,直播帖子等)。我们的方法通过利用(1)在线配置文件中描绘的触点和(2)NLP技术之间的联系人之间的语义等效来提供隐私建议,并分析共享实时流。提出了3年后的最终结果,并不久讨论了对其他环境的可移植性。

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