University of Cambridge > > Computer Laboratory Digital Technology Group (DTG) Meetings > (Research) Privacy Policies for the Individual / (Research) Traffic Signal Data: Prediction using Machine Learning

(Research) Privacy Policies for the Individual / (Research) Traffic Signal Data: Prediction using Machine Learning

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Research: Privacy Policies for the Individual, Sören Preibusch

Privacy policy negotiations allow individually tailored data processing schemes on a per transaction basis. Mandatory data collection is replaced by incentivised data revelation. Consumers are offered choices in privacy-related decision-making; and service providers can leverage customers’ privacy concerns and turn data protection from a business impediment into a competitive advantage. The timing of data collection and the dimensioning of effective incentives are decision parameters in designing and implementing successful privacy negotiations. On Web sites with social networking features, privacy negotiations allow moderation of the quantity and quality of information released, including the topology of the social network itself. Existing standards can be exploited to enable securely and selectively hidden friendship links in a distributed social networking scenario. This talk reviews the impact and design of privacy policy negotiations in centralised and distributed architectures, reports on ongoing work, and sketches future research directions.

Research: Traffic Signal Data: Prediction using Machine Learning, Usman Khan

We evaluate the accuracy of prediction of traffic signal data using a simple Decision Tree learning algorithm (a modification of the simple ID3 Decision Tree learning algorithm).

This talk is part of the Computer Laboratory Digital Technology Group (DTG) Meetings series.

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