TY - GEN
T1 - Social inference risk modeling:In mobile and social applications
AU - Motahari, Sara
AU - Ziavras, Sotirios
AU - Naaman, Mor
AU - Ismail, Mohamed
AU - Jones, Quentin
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - The emphasis of emerging mobile and Web 2.0 applications on collaboration and communication increases threats to user privacy. A serious, yet under-researched privacy risk results from social inferences about user identity, location and other personal information. In this paper, after analyzing the social inference problem theoretically, we assess the extent of the risk to users of computer-mediated communication and location based applications through 1) a laboratory experimentation, 2) a mobile phone field study, and 3) simulation. Our experimentation involved the use of 530 user-created profiles and a 292-subject laboratory chat-study between strangers. The field study explored the patterns of collocation and anonymity of 165 users using a location-aware mobile-phone survey tool. The empirical data was then utilized to populate large-scale simulations of the social inference risk. The work validates the theoretical model, highlights the seriousness of the social inference risk, and shows how the extent and nature of the risk differs for different classes of social computing applications. We conclude with a discussion of the system design implications.
AB - The emphasis of emerging mobile and Web 2.0 applications on collaboration and communication increases threats to user privacy. A serious, yet under-researched privacy risk results from social inferences about user identity, location and other personal information. In this paper, after analyzing the social inference problem theoretically, we assess the extent of the risk to users of computer-mediated communication and location based applications through 1) a laboratory experimentation, 2) a mobile phone field study, and 3) simulation. Our experimentation involved the use of 530 user-created profiles and a 292-subject laboratory chat-study between strangers. The field study explored the patterns of collocation and anonymity of 165 users using a location-aware mobile-phone survey tool. The empirical data was then utilized to populate large-scale simulations of the social inference risk. The work validates the theoretical model, highlights the seriousness of the social inference risk, and shows how the extent and nature of the risk differs for different classes of social computing applications. We conclude with a discussion of the system design implications.
KW - Inference
KW - Privacy
KW - Ubiquitous social computing
UR - http://www.scopus.com/inward/record.url?scp=70849100654&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70849100654&partnerID=8YFLogxK
U2 - 10.1109/CSE.2009.237
DO - 10.1109/CSE.2009.237
M3 - Conference contribution
AN - SCOPUS:70849100654
SN - 9780769538235
T3 - Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009
SP - 125
EP - 132
BT - Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009 - 2009 IEEE International Conference on Privacy, Security, Risk, and Trust, PASSAT 2009
T2 - 2009 IEEE International Conference on Privacy, Security, Risk, and Trust, PASSAT 2009
Y2 - 29 August 2009 through 31 August 2009
ER -