Privacy concerns are an important barrier to the growth of social networks, e-commerce, ubiquitous computing, and location sharing services. The large majority of Internet users takes a pragmatic stance on information disclosure: they trade off the anticipated benefits with the risks of disclosure, a decision process that has been dubbed privacy calculus. Privacy decisions are inherently difficult though, because they have delayed and uncertain repercussions that are difficult to trade-off with the possible immediate gratification of disclosure.
How can we help users to balance the benefits and risks of information disclosure in a user-friendly manner, so that they can make good privacy decisions? Existing research has explored two approaches to this problem, but neither provides a satisfying solution. In my talk I discuss these two approaches, and introduce a new user-tailored approach that provides more user-friendly privacy decision support.