AbstractUsers of social networking services such as Facebook often want to manage thesharing of information and content with diﬀerent groups of people based on theirdiﬀering relationships. The growing popularity of such services has meant thatusers are increasingly faced with the copresence of diﬀerent groups associatedwith diﬀerent aspects of their lives, within their network of contacts. However,few users are utilising the group-based privacy controls provided to them by theSNS provider. In this thesis we examine the reasons behind the lack of use ofgroup-based privacy controls, ﬁnding that it can be largely attributed to thesigniﬁcant burden associated with group conﬁguration. We aim to overcome thisburden by developing automated mechanisms to assist users with many aspectsof group-based privacy control, including initial group conﬁguration, labeling,adjustment and selection of groups for sharing privacy sensitive content.We use a mixed methods approach in order to understand: how automatedmechanisms should be designed in order to support users with their privacy control, how well these mechanisms can be expected to work, what the limitations are, and how such mechanisms aﬀect users’ experiences with social networking services and content sharing. Our results reveal the criteria that SNS users employ in order to conﬁgure their groups for privacy control and illustrate that oﬀ-the-shelf algorithms and techniques which are analogous to these criteria can be used to support users. We show that structural network clustering algorithms provide beneﬁts for initial group conﬁguration and that clustering threshold adjustments and detection of hubs and outliers with the network are necessary for group adjustment. We demonstrate that public proﬁle data can be extracted from the network in order to help users to comprehend their groups, and that contextual information relating to context, contacts, and content can be used to make recommendations about which groups might be useful for disclosure in a given situation. We also show that all of these mechanisms can be used to signiﬁcantly reduce the burden of privacy control and that users react positively to such features.
|Date of Award||1 Aug 2012|
|Supervisor||Eamonn O'Neill (Supervisor)|
- Social network analysis
- human computer interaction
Automating Group-Based Privacy Control in Social Networks
Jones, S. (Author). 1 Aug 2012
Student thesis: Doctoral Thesis › PhD