Policies in BGP are implemented as routing configurations that determine how route information is shared among neighbors to control traffic flows across networks. This process is generally template driven, device centric, limited in its expressibility, time consuming and error prone which can lead to configurations where policies are violated or there are unintended consequences that are difficult to detect and resolve. In this paper, we propose an alternate mechanism for policy based networking that relies on using additional semantic information associated with routes expressed in an OWL ontology. Policies are expressed using SWRL to provide fine-grained control where by the routers can reason over their routes and determine how they need to be exchanged. In this paper, we focus on security related BGP policies and show how our framework can be used in implementing them. Additional contextual information such as affiliations and route restrictions are incorporated into our policy specifications which can then be reasoned over to infer the correct configurations that need to be applied, resulting in a process which is easy to deploy, manage and verify for consistency.
Web search logs are of growing importance to researchers as they help understanding search behavior and search engine performance. However, search logs typically contain sensitive information about users and therefore considerable caution must be exercised when considering releasing the logs to the research community. Current approaches to releasing search logs focus on either protecting the privacy of users or enhancing the utility of data to researchers. In this work, we address the privacy-utility tradeoff by providing safe access to search logs, instead of releasing them. We propose a policy based safe interactive framework built on semantic policies and differential privacy to allow researchers access to search logs, while maintaining the privacy of the users. Semantic policies are used to infer the higher levels of information that can be mined from a dataset based on the fields accessed by a researcher. The accessed fields are then used to build research profile(s) that guide the amount of privacy to be enforced using differential privacy. We show the additional utility that can be obtained in our framework by two demonstrative experiments that involve access to user level information. Our results indicate that valid research can be conducted in our framework without forgoing the privacy of individuals.
The age of technology has changed the way that surgeons are being trained. Traditional methodologies for training can include lecturing, shadowing, apprenticing, and developing skills within live clinical situations. Computerized tools which simulate surgical procedures and/or experiences can allow for "virtual" experiences to enhance the traditional training procedures that can dramatically improve upon the older methods. However, such systems do not to adapt to the training context. We describe a ubiquitous computing system that tracks low-level events in the surgical training room (e.g. student locations, lessons completed, learning tasks assigned, and performance metrics) and from these derive the training context. This can be used to create an adaptive training system.
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