Privacy Notions for Privacy-Preserving Distributed Data Mining: Foundations and Privacy Games

It is well understood that the huge volumes of data captured in recent years have the potential to underpin significant research developments in many fields. But, to realise these benefits, all relevant parties must be comfortable with how this data is shared. At the heart of this is the notion of privacy — which is recognised as being somewhat difficult to define. Previous authors have shown how privacy notions such as anonymity, unlinkability and pseudonymity might be combined into a single formal framework. In this paper we use and extend this work by defining privacy games for individual and group privacy within distributed environments. More precisely, for each privacy notion, we formulate a game that an adversary has to win in order to break the notion. Via these games, we aim to clarify understanding of, and relationships between, different privacy notions; we also aim to give an unambiguous understanding of adversarial actions. Additionally, we extend previous work via the notion of unobservability.