Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that, although OSN capa- bilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make informed sharing decisions.   present Pri- vacy Dynamics, an adaptive architecture that learns privacy norms for dierent audience groups based on users' sharing behaviours. Our architecture is underpinned by a formal model inspired by social identity theory, a social psychology framework for analysing group processes and intergroup re- lations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of con ict rules. In our approach a privacy norm is specied in terms of the information objects that should be prevented fromowing beten two con icting social identity groups.  implement our formal model by using inductive logic programming (ILP), which automati- cally learns privacy norms.  evaluate the performance of our learning approach using synthesised data representing the sharing behaviour of social network users.
['Gul Calikli', 'Arosha K. Bandara', 'Alessandra Russo', 'Luke Dickens', 'Blaine A. Price', 'Avelie Stuart', 'Mark Levine', 'Bashar Nuseibeh']
I am writing a research paper on a idea Privacy dynamics: learning privacy norms for social software
[{"from": "human", "value": "I am writing a research paper on a idea Privacy dynamics: learning privacy norms for social software"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that, although OSN capa- bilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make informed sharing decisions.   present Pri- vacy Dynamics, an adaptive architecture that learns privacy norms for dierent audience groups based on users' sharing behaviours. Our architecture is underpinned by a formal model inspired by social identity theory, a social psychology framework for analysing group processes and intergroup re- lations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of con ict rules. In our approach a privacy norm is specied in terms of the information objects that should be prevented fromowing beten two con icting social identity groups.  implement our formal model by using inductive logic programming (ILP), which automati- cally learns privacy norms.  evaluate the performance of our learning approach using synthesised data representing the sharing behaviour of social network users."}]