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The "privacy by design" philosophy addresses privacy aspects early in the design and development of an information system. While privacy by design solutions often provide considerable advantages over "post hoc" privacy solutions, they are usually not customized to the needs of individual users. Further, research shows that users differ substantially in their privacy management strategies. Thus, how can we support such broad privacy needs in a comprehensive and user-centered way? This paper presents the idea of user-tailored privacy by design, a design methodology that combines multiple privacy features into a single intelligent user interface. We discuss how this methodology moves beyond the "one-size-fits-all" approach of existing privacy by design solutions and the narrow focus on information disclosure of existing user-tailored privacy solutions. We illustrate our approach through an implementation of usertailored privacy by design within Facebook based on six privacy management profiles that were discovered in recent work, and subsequently extend this idea to the context of the Total Learning Architecture (TLA), which is a next generation learning platform that uses pervasive user monitoring to provide highly adaptive learning recommendations.

Research finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed “adaptation methods” (automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that the automation of privacy features afforded users the most privacy protection, while giving privacy suggestions caused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.