Why We're Really Bad at Privacy Decisions (and What To Do About It)

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Privacy issues are an undying obstacle to the real-world implementation of

information systems, from online retailers, to social networks, to smart

home technology. Existing solutions to these privacy issues involve giving

users more control over, and more information about, the privacy settings

provided by these systems. In this talk I will argue that these solutions fail

when users with limited cognitive resources encounter systems with

complex and far-reaching privacy implications. I will subsequently discuss

a novel human-centric solution to improve users' privacy decisions:

User-Tailored Privacy. User-Tailored Privacy is an approach to privacy that

measures users’ privacy-related characteristics and behaviors, uses this

as input to model their privacy preferences, and then provides them with

adaptive privacy decision support. In effect, it applies data science as a

means to support users’ privacy decisions.