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Privacy Support for the Total Learning Architecture Volume 3: Summit Report

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How can we reconcile the need for extensive customizability with users apparent lack of skills and motivation to manage their own privacy settings In this report we investigate User-Tailored Privacy as means to support users privacy decision-making. With User-Tailored Privacy UTP,a system would first measure users privacy-related characteristics and behaviors, use this as input to model their privacy preferences, and then adapt the systems privacy settings to these preferences Figure 1. This adaptation could take the form of a default setting or are recommendation, either with or without an accompanying justification. UTP aims to strike this balance between giving users no control over, or information about, their privacy at all which will be insufficient in highly sensitive situations and may deter privacy minded individuals and giving them full control and information which makes setting ones privacy settings unmanageably complex. Arguably, UTP relieves some of the burden of the privacy decision from the user by providing the right privacy-related information and the right amount of privacy control that is useful, but not overwhelming or misleading. This way, it enables them to make privacy-related decisions within the limits of their bounded rationality. With the research on UTP still in its infancy, it is important that the solutions proposed to ADL have broad support from researchers in the privacy and user modeling community. In November2017, PI Knijnenburg therefore organized a User-Tailored Privacy Summit to bring together a group of interested researchers in an effort to standardize existing UTP approaches. The goal of the summit was to garner broader support for user-tailored privacy and to generate this best practices report.

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Technical Report



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Approved For Public Release;

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