The ability to control and maintain privacy is central to the preservation of identity. In recent years, social psychologists have made a core distinction between personal identity (which refers to what makes us unique, as individuals, compared to other individuals) and social identity (which refers to our sense of ourselves as members of a social group and the meaning that group has for us). In the latter case, our sense of who we are can be derived from our membership of social groups. Identity is not fixed, but is rather the outcome of a dynamic process. We can move from a personal to a social identity (and back again) depending on the context.  Understanding the identity process is therefore key to assessing the impact that privacy and security policies have on people's behaviours. This is essential in order to be able to deliver systems that can express and analyse users' privacy requirements and, at runtime, self-adapt and guide users as they move from context to context. Broadly speaking, our proposed project asks the following two questions and attempts to answer them from both a social psychology and a computing perspective:

  • Can privacy be a distributed quality (across 'the group')?
  • If so, under what conditions might this be the case?
  • Can the group protect the privacy of the individual?
  • If so, how does the group manage the privacy-related behaviour of its members?

At the heart of the project is a hypothesis that individuals are able to better manage their privacy by adopting or learning from the 'wisdom of groups' - we use this term as an acknowledgement of the crowd sourcing movement, also adapted by others in the catchphrase 'wisdom of friends'. Our novelty is in extending this idea to exploit the wisdom of particular subsets of people - groups whose positions and knowledge are more nuanced than a crowd. Our technical challenge is to investigate what we call the privacy dynamics of individuals as they relate to their membership of social, professional or other groups, to develop computational (machine learning) techniques that support such dynamics, and then to deliver privacy management capabilities interactively, autonomously, and adaptively as individuals' contexts change.