Differential Privacy: Now it's Getting Personal

Differential privacy provides a way to get useful information about sensitive data without revealing much about any one individual. It enjoys many nice compositionality properties not shared by other approaches to privacy, including, in particular, robustness against side-knowledge. Designing differentially private mechanisms from scratch can be a challenging task. One way to make it easier to construct new differential private mechanisms is to design a system which allows more complex mechanisms (programs) to be built from differentially private building blocks in principled way, so that the resulting programs are guaranteed to be differentially private by construction. This paper is about a new accounting principle for building differentially private programs. It is based on a simple generalisation of classic differential privacy which we call Personalised Differential Privacy (PDP). In PDP each individual has its own personal privacy level. We describe ProPer, a interactive system ...

• Type: Journal
• Year: 2015
• Where: POPL
• Authors: Hamid Ebadi, David Sands, Gerardo Schneider
• Keywords: POPL 2015, Programming Languages