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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,” https://support.google.com/ ads/answer/2662856, accessed Nov. 21, 2014. [4

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Insurance Dynamics – A Data Mining Approach for Customer Retention in Health Care Insurance Industry

? - Advertising Age, Vol. 9, 1952, 53-55. 8. Fournier, S., J. L. Yao. Reviving Brand Loyalty: A Reconceptualization Within the Framework of Customer-Brand Relationships. - International Journal of Research in Marketing, Vol. 14, 1997, No 5, 451-472. 9. Francis, L. Neural Networks Demystified. Casualty Actuarial Society Forum, 2001, 252-319. 10. Haberman, S., A. E. Renshaw. Actuarial Applications of Generalized Linear Models. - In: D. J. Hand, S. D. Jacka, Eds. London, Statistics in Finance, Arnold, 1998. 11. Han, J

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Data Analytics in CRM Processes: A Literature Review

. [30] S. Palanimalai and I. Paramasivam, “Big Data Analytics Bring New Insights and Higher Business Value - an Experiment Carried Out to Divulge Sales Forecasting Solutions,” International Journal of Advanced Intelligence Paradigms, vol. 8, no. 2, p. 207, 2016. https://doi.org/10.1504/ijaip.2016.075728 [31] A. Kumar, R. Bezawada, R. Rishika, R. Janakiraman, and P. K. Kannan, “From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior,” Journal of Marketing, vol. 80, no. 1, pp. 7-25, Jan. 2016. https://doi.org/10

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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,” https://www.ftc.gov/sites/default/files/documents/reports/privacy-online-fair-information-practices-electronic-marketplace-federal-trade-commission-report/privacy2000.pdf , May 2000. [2] Federal Trade Comm’n, “Self-Regulatory Principles for Online Behavioral Advertising,” https://www.ftc.gov/sites/default/files/documents/reports/federal-trade-commission-staff-report-self-regulatory-principles-online-behavioral-advertising/p085400

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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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The Effects of B2C Interaction on Customer Loyalty

. Labarbera P.A. 2001 Consumer behavior in web-based commerce: An empirical study International Journal of Electronic Commerce 6 2 115 138 Kuehn, A.A. (1962). Consumer brand choice as a learning process. Journal of Advertising Research, 2(4), 10–17. Kuehn A.A. 1962 Consumer brand choice as a learning process Journal of Advertising Research 2 4 10 17 Lee, J. (2017). The assessment tool for user perceived interactivity from ACG website interactivity on imagination. Communications in Computer and Information Science, 713, 57–65. 10.1007/978-3-319-58750-9_8 Lee J. 2017 The

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Digitizing Dunhuang Cultural Heritage: A User Evaluation of Mogao Cave Panorama Digital Library

for the Web presence of local heritage, on the aspects of usability, content, aesthetics, and Web 2.0 services. Together with interviews with local study librarians, their findings suggest that, among other things, researchers and practitioners should continue developing knowledge of users’ expectations and information seeking behaviors, which can inform decision making on preservation practice, strategy, and policies ( Smith & Rowley, 2012 ). In their framework of “information representation design,” Abbas et al. (2016) also argued that, in the spirit of user

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Twitter Users’ Privacy Concerns: What do Their Accounts’ First Names Tell Us?

objectives of market segmentation is to figure out the preferences of the users in order to provide a more personalized service without the need of conducting direct dialogues. If the segmentation is made properly, the company will reduce the cost of targeted advertising, offer more personalized service, and potentially increase its revenues. For example, previous work has shown that gender plays an important role in the purchase decision-making. Van Aswegen explains that shopping behavior in men and women is highly affected by their considerations, concerns, perspectives

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

References [1] Adblock plus. https://adblockplus.org/. [2] Blur. http://www.abine.com/. [3] Ghostery. https://www.ghostery.com/en/. [4] Open directory project. http://www.dmoz.org. Accessed: Nov, 2014. [5] W3C Do Not Track Standard. http://www.w3.org/TR/2015/WD-tracking-compliance-20150714/. [6] Acceptable ads. https://acceptableads.org/, 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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