Search Results

1 - 10 of 14 items :

  • "privacy preferences" x
Clear All

. 2013. Dimensionality of information disclosure behavior. International Journal of Human-Computer Studies 71, 12 (2013), 1144–1162. [19] Scott Lederer, Jennifer Mankoff, and Anind K Dey. 2003. Who wants to know what when? privacy preference determinants in ubiquitous computing. In CHI’03 extended abstracts on Human factors in computing systems . ACM, 724–725. [20] Hosub Lee and Alfred Kobsa. 2016. Understanding user privacy in Internet of Things environments. In IEEE WF-IoT 2016 . 407–412. [21] Jialiu Lin, Shahriyar Amini, Jason I Hong, Norman Sadeh, Janne

whether or not any part of the account name can be found in a directory of English names ( Khazaei et al., 2016b ). The value of this field is one if any part of the account name does appear in the name directory, or zero if it does not. Our second null hypothesis was that whether or not the name exists in the name dictionary is independent of the user’s privacy setting. Lastly, we explored the correlation between a user account’s privacy setting and the inferred gender, and then added the inferred gender feature to Khazaei et al. (2016a) ’s privacy preference

secure experience for our users. https://android-developers.googleblog.com/2018/10/providing-safe-and-secure-experience.html , Oct 2018. Accessed: 2019-02-24. [6] Douglas Bates, Martin Mächler, Ben Bolker, and Steve Walker. Fitting linear mixed-effects models using lme4. Journal of Statistical Software , 67(1):1–48, 2015. [7] Michael Benisch, Patrick Gage Kelley, Norman Sadeh, and Lorrie Faith Cranor. Capturing location-privacy preferences: Quantifying accuracy and user-burden tradeoffs. Personal Ubiquitous Comput. , 15(7):679–694, October 2011. [8] Bram Bonné, Sai

Abstract

In many real world scenarios, terms of service allow a producer of a service to collect data from its users. Producers value data but often only compensate users for their data indirectly with reduced prices for the service. This work considers how a producer (data analyst) may offer differential privacy as a premium service for its users (data subjects), where the degree of privacy offered may itself depend on the user data. Along the way, it strengthens prior negative results for privacy markets to the pay-for-privacy setting and develops a new notion of endogenous differential privacy. A positive result for endogenous privacy is given in the form of a class of mechanisms for privacy-as-a-service markets that 1) determine ɛ using the privacy and accuracy preferences of a heterogeneous body of data subjects and a single analyst, 2) collect and distribute payments for the chosen level of privacy, and 3) privately analyze the database. These mechanisms are endogenously differentially private with respect to data subjects’ privacy preferences as well as their private data, they directly elicit data subjects’ true preferences, and they determine a level of privacy that is efficient given all parties’ preferences.

. Benisch, P. G. Kelley, N. Sadeh, and L. Cranor. Capturing Location-Privacy Preferences: Quantifying Accuracy and User-Burden Tradeoffs. Technical Report CMU-ISR-10-105, 2010. [9] I. Bilogrevic, K. Huguenin, B. Agir, M. Jadliwala, and J.-P. Hubaux. Adaptive Information-sharing for Privacy-aware Mobile Social Networks. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing , UbiComp ’13, pages 657–666, New York, NY, USA, 2013. ACM. [10] S. S. Brehm and J. W. Brehm. Psychological Reactance: A Theory of Freedom and Control

’ mobile app privacy preferences: Restoring usability in a sea of permission settings. In Symposium on Usable Privacy and Security (SOUPS), 2014. [14] Bin Liu, Jialiu Lin, and Norman Sadeh. Reconciling mobile app privacy and usability on smartphones: could user privacy profiles help? In Proceedings of the 23rd international conference on World wide web, pages 201-212. ACM, 2014. [15] Jialiu Lin, Guang Xiang, Jason I. Hong, and Norman Sadeh. Modeling people’s place naming preferences in location sharing. In Proceedings of the 12th ACM International Conference on

: understanding users’ mental models of mobile app privacy through crowdsourcing. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing , pages 501–510. ACM, 2012. [33] J. Lin, B. Liu, N. Sadeh, and J. I. Hong. Modeling users’ mobile app privacy preferences: Restoring usability in a sea of permission settings. In 10th Symposium On Usable Privacy and Security (SOUPS 2014) , pages 199–212, Menlo Park, CA, 2014. USENIX Association. [34] B. Liu, M. S. Andersen, F. Schaub, H. Almuhimedi, S. Zhang, N. Sadeh, A. Acquisti, and Y. Agarwal. Follow my recommendations: A

://www.cookiebot.com/en/wordpress-cookie-plugin/ [17] M. Ackerman, L. F. Cranor, J. Reagle. Privacy in e-commerce: Examining user scenarios and privacy preferences. In: Conference on Electronic Commerce (EC) (ACM, 1999) 1–8 [18] L. I. Millett, B. Friedman, E. Felten. Cookies and web browser design: Toward realizing informed consent online. In: Conference on Human Factors in Computing System (CHI) (ACM, 2001) 46–52 [19] F. Schaub, R. Balebako, A. L. Durity, L. F. Cranor. A design space for effective privacy notices. In: Symposium On Usable Privacy and Security (SOUPS) (USENIX, 2015) 1–17 [20] M. Bergmann. Generic

accommodating crowdfunder privacy preferences: a randomized field experiment. Management Science , 61(5), 949-962. Burtch, G., Ghose, A., & Wattal, S. (2014). Cultural differences and geography as determinants of online prosocial lending. MIS Quarterly , 38(3), 773-794. Calic, G., & Mosakowski, E. (2016). Kicking off social entrepreneurship: how a sustainability orientation influences crowdfunding success. Journal of Management Studies , 53(5), 738-767. Cecere, G., Le Guel, F., & Rochelandet, F. (2017). Crowdfunding and social influence: an empirical investigation

References [1] M. S. Ackerman, L. F. Cranor, and J. Reagle. Privacy in ecommerce: examining user scenarios and privacy preferences. In Proceedings of the 1st ACM conference on Electronic commerce , pages 1–8. ACM, 1999. [2] A. Acquisti and R. Gross. Imagined communities: Awareness, information sharing, and privacy on the facebook. In Privacy enhancing technologies , pages 36–58. Springer, 2006. [3] A. Acquisti and J. Grossklags. Privacy and rationality in individual decision making. IEEE Security & Privacy , 2:24–30, 2005. [4] I. Ajzen and M. Fishbein