More Web Proxy on the site http://driver.im/

Article

Capturing Social Networking Privacy Preferences

Authors:

Ramprasad Ravichandran,

Michael Benisch,

Patrick Gage Kelley,

Norman M. SadehAuthors Info & Claims

PETS '09: Proceedings of the 9th International Symposium on Privacy Enhancing Technologies

Pages 1 - 18

https://doi.org/10.1007/978-3-642-03168-7_1

Published: 27 July 2009 Publication History

Abstract

Social networking sites such as Facebook and MySpace thrive on the exchange of personal content such as pictures and activities. These sites are discovering that people's privacy preferences are very rich and diverse. In theory, providing users with more expressive settings to specify their privacy policies would not only enable them to better articulate their preferences, but could also lead to greater user burden. In this article, we evaluate to what extent providing users with default policies can help alleviate some of this burden. Our research is conducted in the context of location-sharing applications, where users are expected to specify conditions under which they are willing to let others see their locations. We define canonical policies that attempt to abstract away user-specific elements such as a user's default schedule, or canonical places, such as "work" and "home." We learn a set of default policies from this data using decision-tree and clustering algorithms. We examine trade-offs between the complexity / understandability of default policies made available to users, and the accuracy with which they capture the ground truth preferences of our user population. Specifically, we present results obtained using data collected from 30 users of location-enabled phones over a period of one week. They suggest that providing users with a small number of canonical default policies to choose from can help reduce user burden when it comes to customizing the rich privacy settings they seem to require.

References

[1]

Duine project, http://www.telin.nl/project/Home.cfm?id=387&language=en

[2]

Loopt, http://www.loopt.com

[3]

Mobikade, http://www.mkade.com

[4]

Mobimii, http://www.mobimii.com

[5]

Wikimapia, http://www.wikimapia.com

[6]

Barkhuus, L., Dey, A.K.: Location-based services for mobile telephony: a study of users' privacy concerns. In: Rauterberg, M., Menozzi, M., Wesson, J. (eds.) INTERACT. IOS Press, Amsterdam (2003).

[7]

Benisch, M., Kelley, P.G., Sadeh, N., Sandholm, T., Cranor, L.F., Drielsma, P.H., Tsai, J.: The impact of expressiveness on the effectiveness of privacy mechanisms for location sharing. Technical Report CMU-ISR-08-141, Carnegie Mellon University (2008).

[8]

Breese, J.S., Heckerman, D., Kadie, C.: In: Empirical Analysis of Predictive Algorithms for Collaborative Filtering (1998).

[9]

Consolvo, S., Smith, I.E., Matthews, T., LaMarca, A., Tabert, J., Powledge, P.: Location disclosure to social relations: why, when, & what people want to share. In: CHI 2005: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 81-90. ACM, New York (2005).

[10]

Faltings, B., Pu, P., Torrens, M., Viappiani, P.: Designing example-critiquing interaction. In: Vanderdonckt, J., Nunes, N.J., Rich, C. (eds.) Intelligent User Interfaces, pp. 22-29. ACM, New York (2004).

[11]

Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 230-237. ACM, New York (1999).

[12]

Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89-115 (2004).

[13]

Hong, J.I., Landay, J.A.: An architecture for privacy-sensitive ubiquitous computing. In: MobiSys 2004: Proceedings of the 2nd international conference on Mobile systems, applications, and services, pp. 177-189. ACM, New York (2004).

[14]

Kaasinen, E.: User needs for location-aware mobile services. Personal Ubiquitous Comput 7(1), 70-79 (2003).

[15]

Kawsar, F., Nakajima, T.: Persona: a portable tool for augmenting proactive applications with multimodal personalization support. In: MUM 2007: Proceedings of the 6th international conference onMobile and ubiquitous multimedia, pp. 160-168. ACM, New York (2007).

[16]

Kelley, P.G., Drielsma, P.H., Sadeh, N.M., Cranor, L.F.: User-controllable learning of security and privacy policies. In: Balfanz, D., Staddon, J. (eds.) AISec, pp. 11-18. ACM, New York (2008).

[17]

Ashraf, K., Kay, C.: Context-aware telephony: privacy preferences and sharing patterns. In: CSCW 2006: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pp. 469-478. ACM, New York (2006).

[18]

Scott, L., Jennifer, M., Dey, A.K.: Who wants to know what when? privacy preference determinants in ubiquitous computing. In: CHI 2003: CHI 2003 extended abstracts on Human factors in computing systems, pp. 724-725. ACM, New York (2003).

[19]

Quinlan, R.J.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann, San Francisco (1993).

[20]

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work, pp. 175-186. ACM Press, New York (1994).

[21]

Sadeh, N., Gandon, F., Kwon, O.B.: Ambient intelligence: The mycampus experience. Technical Report CMU-ISRI-05-123, Carnegie Mellon University (2005).

[22]

Sadeh, N., Hong, J., Cranor, L., Fette, I., Kelley, P., Prabaker, M., Rao, J.: Understanding and capturing peoples privacy policies in a mobile social networking application. Personal and Ubiquitous Computing.

[23]

Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: ICML 2007: Proceedings of the 24th international conference on Machine learning, pp. 791-798. ACM, New York (2007).

[24]

Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms (2001).

[25]

Want, R., Hopper, A., Falcao, V., Gibbons, J.: The active badge location system. ACM Trans. Inf. Syst. 10(1), 91-102 (1992).

Cited By

Mendes RCunha MVilela JBeresford A(2022)Enhancing User Privacy in Mobile Devices Through Prediction of Privacy PreferencesComputer Security – ESORICS 202210.1007/978-3-031-17140-6_8(153-172)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17140-6_8
Bahirat PHe YMenon AKnijnenburg BBerkovsky SHijikata YRekimoto JBurnett MBillinghurst MQuigley A(2018)A Data-Driven Approach to Developing IoT Privacy-Setting Interfaces23rd International Conference on Intelligent User Interfaces10.1145/3172944.3172982(165-176)Online publication date: 5-Mar-2018
https://dl.acm.org/doi/10.1145/3172944.3172982
Wang FYun CGoldwasser SVaikuntanathan VZaharia MAkella AHowell J(2017)SplinterProceedings of the 14th USENIX Conference on Networked Systems Design and Implementation10.5555/3154630.3154654(299-313)Online publication date: 27-Mar-2017
https://dl.acm.org/doi/10.5555/3154630.3154654
Show More Cited By

Capturing Social Networking Privacy Preferences
1. Security and privacy
2. Social and professional topics
  1. Computing / technology policy

Recommendations

Exploiting vulnerability to secure user privacy on a social networking site
KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

As (one's) social network expands, a user's privacy protection goes beyond his privacy settings and becomes a social networking problem. In this research, we aim to address some critical issues related to privacy protection: Would the highest privacy ...
Determining user privacy preferences by asking the right questions: an automated approach
WPES '05: Proceedings of the 2005 ACM workshop on Privacy in the electronic society

One fundamental aspect of user privacy is to respect the privacy preferences that users have. A clear prerequisite to doing this is accurately gauging what user's privacy preferences are. Current approaches either offer limited privacy options or have ...
Generating default privacy policies for online social networks
CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems

Default privacy policies have a significant impact on the overall dynamics and success of online social networks, as users tend to keep their initial privacy policies. In this work-in-progress, we present a new method for suggesting privacy policies for ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

PETS '09: Proceedings of the 9th International Symposium on Privacy Enhancing Technologies

July 2009

253 pages

ISBN:9783642031670

Editors:
Ian Goldberg
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada N2L 3G1
,
Mikhail J. Atallah
Department of Computer Science, Purdue University, West Lafayete, USA IN 47907-2107

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 27 July 2009

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mendes RCunha MVilela JBeresford A(2022)Enhancing User Privacy in Mobile Devices Through Prediction of Privacy PreferencesComputer Security – ESORICS 202210.1007/978-3-031-17140-6_8(153-172)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17140-6_8
Bahirat PHe YMenon AKnijnenburg BBerkovsky SHijikata YRekimoto JBurnett MBillinghurst MQuigley A(2018)A Data-Driven Approach to Developing IoT Privacy-Setting Interfaces23rd International Conference on Intelligent User Interfaces10.1145/3172944.3172982(165-176)Online publication date: 5-Mar-2018
https://dl.acm.org/doi/10.1145/3172944.3172982
Wang FYun CGoldwasser SVaikuntanathan VZaharia MAkella AHowell J(2017)SplinterProceedings of the 14th USENIX Conference on Networked Systems Design and Implementation10.5555/3154630.3154654(299-313)Online publication date: 27-Mar-2017
https://dl.acm.org/doi/10.5555/3154630.3154654
Hirschprung RToch ESchwartz-Chassidim HMendel TMaimon O(2017)Analyzing and Optimizing Access Control Choice Architectures in Online Social NetworksACM Transactions on Intelligent Systems and Technology10.1145/30466768:4(1-22)Online publication date: 11-May-2017
https://dl.acm.org/doi/10.1145/3046676
Wisniewski PVitak JPage XKnijnenburg BWang YFiesler CLee CPoltrock SBarkhuus LBorges MKellogg W(2017)In Whose Best Interest?Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/3022198.3022660(377-382)Online publication date: 25-Feb-2017
https://dl.acm.org/doi/10.1145/3022198.3022660
Dong CJin HKnijnenburg B(2016)PPM: A Privacy Prediction Model for Online Social NetworksSocial Informatics10.1007/978-3-319-47874-6_28(400-420)Online publication date: 11-Nov-2016
https://dl.acm.org/doi/10.1007/978-3-319-47874-6_28
Watson JLipford HBesmer A(2015)Mapping User Preference to Privacy Default SettingsACM Transactions on Computer-Human Interaction10.1145/281125722:6(1-20)Online publication date: 2-Nov-2015
https://dl.acm.org/doi/10.1145/2811257
Hirschprung RToch EMaimon O(2015)Simplifying Data Disclosure Configurations in a Cloud Computing EnvironmentACM Transactions on Intelligent Systems and Technology10.1145/27004726:3(1-26)Online publication date: 30-Apr-2015
https://dl.acm.org/doi/10.1145/2700472
Wisniewski PIslam AKnijnenburg BPatil SCosley DForte ACiolfi LMcDonald D(2015)Give Social Network Users the Privacy They WantProceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing10.1145/2675133.2675256(1427-1441)Online publication date: 28-Feb-2015
https://dl.acm.org/doi/10.1145/2675133.2675256
Lin JLiu BSadeh NHong J(2014)Modeling users' mobile app privacy preferencesProceedings of the Tenth USENIX Conference on Usable Privacy and Security10.5555/3235838.3235856(199-212)Online publication date: 9-Jul-2014
https://dl.acm.org/doi/10.5555/3235838.3235856
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents