In system monitoring, one is often interested in checking properties of aggregated data. Current policy monitoring approaches are limited in the kinds of aggregations they handle. To rectify this, we extend an expressive language, metric first-order temporal logic, with aggregation operators. Our extension is inspired by the aggregation operators common in database query languages like SQL. We provide a monitoring algorithm for this enriched policy specification language. We show that, in comparison to related data processing approaches, our language is better suited for expressing policies, and our monitoring algorithm has competitive performance.
Abstract-Recent years have seen a significant increase in the popularity of social networking services. These online services enable users to construct groups of contacts, referred to as friends, with which they can share digital content and communicate. This sharing is actively encouraged by the social networking services, with users' privacy often seen as a secondary concern. In this paper we first propose a privacy-aware social networking service and then introduce a collaborative approach to authoring privacy policies for the service. In addressing user privacy, our approach takes into account the needs of all parties affected by the disclosure of information and digital content.
Databases can leak confidential information when users combine query results with probabilistic data dependencies and prior knowledge. Current research offers mechanisms that either handle a limited class of dependencies or lack tractable enforcement algorithms. We propose a foundation for Database Inference Control based on PROBLOG, a probabilistic logic programming language. We leverage this foundation to develop ANGERONA, a provably secure enforcement mechanism that prevents information leakage in the presence of probabilistic dependencies. We then provide a tractable inference algorithm for a practically relevant fragment of PROBLOG. We empirically evaluate ANGERONA's performance showing that it scales to relevant security-critical problems.
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