Estimation of privacy risk through centrality metrics

Abstract Users are not often aware of privacy risks and disclose information in online social networks. They do not consider the audience that will have access to it or the risk that the information continues to spread and may reach an unexpected audience. Moreover, not all users have the same perception of risk. To overcome these issues, we propose a Privacy Risk Score (PRS) that: (1) estimates the reachability of an user’s sharing action based on the distance between the user and the potential audience; (2) is described in levels to adjust to the risk perception of individuals; (3) does not require the explicit interaction of individuals since it considers information flows; and (4) can be approximated by centrality metrics for scenarios where there is no access to data about information flows. In this case, if there is access to the network structure, the results show that global metrics such as closeness have a high degree of correlation with PRS. Otherwise, local and social centrality metrics based on ego-networks provide a suitable approximation to PRS. The results in real social networks confirm that local and social centrality metrics based on degree perform well in estimating the privacy risk of users.

[1]  P. Bonacich.  Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  Shyan-Ming Yuan,et al.  A small world based overlay network for improving dynamic load-balancing , 2015, J. Syst. Softw..

[4]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[5]  Miguel Rebollo,et al.  Does the Type of Event Influence How User Interactions Evolve on Twitter? , 2015, PloS one.

[6]  Mile Šikić,et al.  Epidemic centrality — is there an underestimated epidemic impact of network peripheral nodes? , 2011, 1110.2558.

[7]  Harpreet Kaur,et al.  Implementation of Enhanced Graph Layout Algorithm for Visualizing Social Network Data using NetworkX Library , 2017 .

[8]  Andrea Landherr,et al.  A Critical Review of Centrality Measures in Social Networks , 2010, Bus. Inf. Syst. Eng..

[9]  Liang Zhao,et al.  Machine Learning in Complex Networks , 2016, Springer International Publishing.

[10]  Bashar Nuseibeh,et al.  Adaptive Sharing for Online Social Networks: A Trade-off Between Privacy Risk and Social Benefit , 2014, 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications.

[11]  Yong Wang,et al.  SONET: A SOcial NETwork Model for Privacy Monitoring and Ranking , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[12]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[13]  Yi-Cheng Zhang,et al.  Leaders in Social Networks, the Delicious Case , 2011, PloS one.

[14]  Laura Ricci,et al.  Distributed protocols for Ego Betweenness Centrality computation in DOSNs , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[15]  Wanlei Zhou,et al.  Using epidemic betweenness to measure the influence of users in complex networks , 2017, J. Netw. Comput. Appl..

[16]  Joseph T. Lizier,et al.  Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: A walk counting approach , 2012, 1203.0502.

[17]  Daniel G. Goldstein,et al.  The structure of online diffusion networks , 2012, EC '12.

[18]  Glenn Lawyer,et al.  Understanding the influence of all nodes in a network , 2015, Scientific Reports.

[19]  Ulrik Brandes,et al.  Centrality Measures Based on Current Flow , 2005, STACS.

[20]  Kristina Lerman,et al.  Limited Attention and Centrality in Social Networks , 2013, 2013 International Conference on Social Intelligence and Technology.

[21]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[22]  Damon Centola,et al.  The Spread of Behavior in an Online Social Network Experiment , 2010, Science.

[23]  Qiang Guo,et al.  Locating influential nodes via dynamics-sensitive centrality , 2015, Scientific Reports.

[24]  Berker Agir,et al.  Adaptive information-sharing for privacy-aware mobile social networks , 2013, UbiComp.

[25]  Martin Everett,et al.  Ego network betweenness , 2005, Soc. Networks.

[26]  Delphine Christin,et al.  Privacy in mobile participatory sensing , 2016 .

[27]  Robert G. Capra,et al.  Factors mediating disclosure in social network sites , 2011, Comput. Hum. Behav..

[28]  Mohamed Shehab,et al.  Semi-supervised policy recommendation for online social networks , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[29]  Kristen LeFevre,et al.  Privacy wizards for social networking sites , 2010, WWW '10.

[30]  Raymond K. Wong,et al.  Privacy Scoring of Social Network Users as a Service , 2015, 2015 IEEE International Conference on Services Computing.

[31]  Arkaitz Zubiaga,et al.  Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads , 2015, PloS one.

[32]  Nadeem Akhtar,et al.  Social Network Analysis Tools , 2014, 2014 Fourth International Conference on Communication Systems and Network Technologies.

[33]  Duanbing Chen,et al.  Identifying Influential Spreaders by Weighted LeaderRank , 2013, ArXiv.

[34]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[35]  Evimaria Terzi,et al.  A Framework for Computing the Privacy Scores of Users in Online Social Networks , 2009, ICDM.

[36]  Luciano da Fontoura Costa,et al.  The role of centrality for the identification of influential spreaders in complex networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  Pierre Lemarinier,et al.  Agent Based Modelling and Simulation tools: A review of the state-of-art software , 2017, Comput. Sci. Rev..

[38]  Rosanna E. Guadagno,et al.  Make new friends or keep the old: Gender and personality differences in social networking use , 2012, Comput. Hum. Behav..

[39]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[40]  S. Desmarais,et al.  Hey Mom, What’s on Your Facebook? Comparing Facebook Disclosure and Privacy in Adolescents and Adults , 2012 .

[41]  Peter V. Marsden,et al.  Egocentric and sociocentric measures of network centrality , 2002, Soc. Networks.

[42]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[43]  Robin D. Burke,et al.  Recommender Systems Based on Social Networks , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[44]  Joshua Fogel,et al.  Internet social network communities: Risk taking, trust, and privacy concerns , 2009, Comput. Hum. Behav..

[45]  Bashar Nuseibeh,et al.  Privacy Dynamics: Learning Privacy Norms for Social Software , 2016, 2016 IEEE/ACM 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).

[46]  Remco van der Hofstad,et al.  Random Graphs and Complex Networks , 2016, Cambridge Series in Statistical and Probabilistic Mathematics.

[47]  P. Erdös,et al.  Graph Theory and Probability , 1959 .

[48]  C. Haythornthwaite.  Social network analysis: An approach and technique for the study of information exchange☆ , 1996 .

[49]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[50]  Pinar Yolum,et al.  Negotiating Privacy Constraints in Online Social Networks , 2015, CARE/MFSC@AAMAS.

[51]  L. Freeman,et al.  Centrality in valued graphs: A measure of betweenness based on network flow , 1991 .

[52]  Béla Bollobás,et al.  Modern Graph Theory , 2002, Graduate Texts in Mathematics.

[53]  Delphine Reinhardt.  Privacy in mobile participatory sensing: Current trends and future challenges , 2016, J. Syst. Softw..

[54]  Guido Caldarelli,et al.  Scale-Free Networks , 2007 .

[55]  Zhiming Zheng,et al.  Searching for superspreaders of information in real-world social media , 2014, Scientific Reports.

[56]  Pinar Yolum,et al.  PROTOSS: A Run Time Tool for Detecting Privacy Violations in Online Social Networks , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[57]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .