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Automated Experiments on Ad Privacy Settings
A Tale of Opacity, Choice, and Discrimination

References [1] J. R. Mayer and J. C. Mitchell, “Third-party web tracking: Policy and technology,” in IEEE Symposium on Security and Privacy, 2012, pp. 413-427. [2] B. Ur, P. G. Leon, L. F. Cranor, R. Shay, and Y. Wang, “Smart, useful, scary, creepy: Perceptions of online behavioral advertising,” in Proceedings of the Eighth Symposium on Usable Privacy and Security. ACM, 2012, pp. 4:1-4:15. [3] Google, “About ads settings,” ads/answer/2662856, accessed Nov. 21, 2014. [4

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Cross-Device Tracking: Measurement and Disclosures

References [1] Federal Trade Comm’n, “Privacy Online: Fair Information Practices in the Electronic Marketplace,” , May 2000. [2] Federal Trade Comm’n, “Self-Regulatory Principles for Online Behavioral Advertising,”

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“Won’t Somebody Think of the Children?” Examining COPPA Compliance at Scale

. [14] X. Cai and X. Zhao. Online Advertising on Popular Children’s Websites: Structural Features and Privacy Issues. Computers in Human Behavior, 2013. [15] P. Carter, C. Mulliner, M. Lindorfer, W. Robertson, and E. Kirda. CuriousDroid: Automated User Interface Interaction for Android Application Analysis Sandboxes. In Proc. of FC, 2016. [16] L. Cavallaro, P. Saxena, and R. Sekar. On the Limits of Information Flow Techniques for Malware Analysis and Containment. In Proc. of DIMVA, pages 143-163. Springer- Verlag, 2008

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An Influence Prediction Model for Microblog Entries on Public Health Emergencies

insight into public opinions during public health events ( Finch et al., 2016 ). The microblog users who contribute information during crises play the role of “digital volunteers” ( Starbird & Palen, 2011 ). The massive information storage capacity of the microblog platforms provides sufficient open source information for relevant studies on public health emergencies. So far, scholars have conducted a huge volume of research using the information related to public emergencies, such as emergency monitoring, emergency information dissemination, and user behavior during

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(Do Not) Track Me Sometimes: Users’ Contextual Preferences for Web Tracking

References [1] Adblock plus. [2] Blur. [3] Ghostery. [4] Open directory project. Accessed: Nov, 2014. [5] W3C Do Not Track Standard. [6] Acceptable ads., 2015. [7] AAAA, ANA, BBB, DNA, and IAB. Self-regulatory principles for online behavioral advertising. Digital

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Diffusion of User Tracking Data in the Online Advertising Ecosystem

Kurt Rothermel. User centric walk: An integrated approach for modeling the browsing behavior of users on the web. In Annual Symposium on Simulation , April 2005. [15] Aaron Cahn, Scott Alfeld, Paul Barford, and S. Muthukrishnan. An empirical study of web cookies. In Proc. of WWW , 2016. [16] Juan Miguel Carrascosa, Jakub Mikians, Ruben Cuevas, Vijay Erramilli, and Nikolaos Laoutaris. I always feel like somebody’s watching me: Measuring online behavioural advertising. In Proc. of ACM CoNEXT , 2015. [17] Big Commerce. Understanding Impressions in

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On the Privacy and Security of the Ultrasound Ecosystem

, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. How much can behavioral targeting help online advertising? In Proceedings of the 18th international conference on World wide web , pages 261–270. ACM, 2009. [51] Y. Yuan, F. Wang, J. Li, and R. Qin. A survey on real time bidding advertising. In Service Operations and Logistics, and Informatics (SOLI), 2014 IEEE International Conference on , pages 418–423. IEEE, 2014. [52] W. Zhang, L. Chen, and J. Wang. Implicit look-alike modelling in display ads: Transfer collaborative filtering to ctr estimation. arXiv

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Why Privacy Is All But Forgotten
An Empirical Study of Privacy & Sharing Attitude

westin’s and altman’s theories of privacy. Journal of Social Issues , 59(2):411–429, 2003. [62] J. D. Morris, C. Woo, J. A. Geason, and J. Kim. The power of affect: Predicting intention. Journal of Advertising Research , 42(3):7–17, 2002. [63] K. A. Neuendorf. The content analysis guidebook . Sage, 2002. [64] P. A. Norberg, D. R. Horne, and D. A. Horne. The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs , 41(1):100–126, 2007. [65] A. Nosko, E. Wood, and S. Molema. All about me

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Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning

. Shay, Y. Wang, R. Balebako, and L. Cranor.Why Johnny Can’T Opt out: A Usability Evaluation of Tools to Limit Online Behavioral Advertising. In SIGCHI, 2012. [40] B. Liu, Y. Dai, X. Li, W. S. Lee, and P. S. Yu. Building Text Classifiers Using Positive and Unlabeled Examples.ICDM, 2003. [41] J. R. Mayer and J. C. Mitchell. Third-Party Web Tracking: Policy and Technology. IEEE S&P, 2012. [42] H. Metwalley, S. Traverso, and M. Mellia. The Online Tracking Horde: a View from Passive Measurements. TMA, 2015

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Are You Sure You Want to Contact Us? Quantifying the Leakage of PII via Website Contact Forms

://, 2010. [37] S. Sunam. Google Plus post on December 8, 2012, (accessed February 23, 2015). [38] The Wall Street Journal. What They Know. [39] Trend Micro Site Safety Center. [40] B. Ur, P. G. Leon, L. F. Cranor, R. Shay, and Y. Wang. Smart, useful, scary, creepy: Perceptions of online behavioral advertising. In Proceedings of the

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