Modeling profile-attribute disclosure in online social networks from a game theoretic perspective

Privacy settings are a crucial part of any online social network as users are confronted with determining which and how many profile attributes to disclose. Revealing more attributes increases users chances of finding friends and yet leaves users more vulnerable to dangers such as identity theft. In this dissertation, we consider the problem of finding the optimal strategy for the disclosure of user attributes in social networks from a game-theoretic perspective. We model the privacy settings\u27 dynamics of social networks with three game-theoretic approaches. In a two-user game, each user selects an ideal number of attributes to disclose to each other according to a utility function. We extend this model with a basic evolutionary game to observe how much of their profiles users are comfortable with revealing, and how this changes over time. We then consider a weighted evolutionary game to investigate the influence of attribute importance, benefit, risk and the network topology on the users\u27 attribute disclosure behavior. The two-user game results show how one user\u27s privacy settings are influenced by the settings of another user. The basic evolutionary game results show that the higher the motivation to reveal attributes, the longer users take to stabilize their privacy settings. Results from the weighted evolutionary game show that: irrespective of risk, users are more likely to reveal their most important attributes than their least important. attributes; when the users\u27 range of influence is increased, the risk factor plays a smaller role in attribute disclosure; the network topology exhibits a considerable effect on the privacy in an environment with risk. Motivation and risk are identified as important factors in determining how efficiently stability of privacy settings is achieved and what settings users will adopt given different parameters. Additionally, the privacy settings are affected by the network topology and the importance users attach to specific attributes. Our models indicate that users of social networks eventually adopt profile settings that provide the highest possible privacy if there is any risk, despite how high the motivation to reveal attributes is. The provided models and the gained results are particularly important to social network designers and providers because they enable us to understand the influence of different factors on users\u27 privacy choices

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Privacy fatigue: The effect of privacy control complexity on consumer electronic information disclosure

When online social networks change privacy control features (i.e. methods of sharing consumer information), the result is often media attention and public outcry. Facebook introduced new privacy controls in 2009 causing the Electronic Frontier Foundation to accuse them of pushing users to disclose more information than ever before. However, there is little research to indicate that such practices are effective. Although research on privacy control designs is emerging, few studies adopt theoretical bases or empirically test the results of the design. This study fills a theoretical and methodological gap in the context of privacy controls. We adopt feature fatigue theory from the marketing literature to explain the effects of privacy control complexity on consumer self-disclosure behavior. We test our model with a unique longitudinal field experiment wherein participants are randomly assigned to various treatments of privacy control complexity. We found support for our theoretical extension we term privacy fatigue

Spreading processes in Multilayer Networks

Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.Comment: 21 pages, 3 figures, 4 table

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse