Differential Privacy

Differential privacy is a formal model of privacy protection that has received sustained attention from the research community, whose work has shown that it is possible to reveal accurate information about a population while rigorously protecting the privacy of its constituents. While DP offers a compelling promise, organizations that choose to adopt it as their privacy standard face a number of challenges doing so.

We present our work on the suitability of the metaphors for aiding informed decisions of data subjects on sharing their data with differential privacy (DP) systems and discuss open research challenges.

Data privacy is an ever important aspect of data analyses. Historically, a plethora of privacy techniques have been introduced to protect data, but few have stood the test of time. From investigating the overlap between big data research, and security and privacy research, I have found that _differential privacy_ presents itself as a promising defender of data privacy.