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

article

Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs

Authors:

Michael Benisch,

Patrick Gage Kelley,

Lorrie Faith CranorAuthors Info & Claims

Personal and Ubiquitous Computing, Volume 15, Issue 7

Pages 679 - 694

https://doi.org/10.1007/s00779-010-0346-0

Published: 01 October 2011 Publication History

Abstract

We present a 3-week user study in which we tracked the locations of 27 subjects and asked them to rate when, where, and with whom they would have been comfortable sharing their locations. The results of analysis conducted on over 7,500 h of data suggest that the user population represented by our subjects has rich location-privacy preferences, with a number of critical dimensions, including time of day, day of week, and location. We describe a methodology for quantifying the effects, in terms of accuracy and amount of information shared, of privacy-setting types with differing levels of complexity (e.g., setting types that allow users to specify location- and/or time-based rules). Using the detailed preferences we collected, we identify the best possible policy (or collection of rules granting access to one's location) for each subject and privacy-setting type. We measure the accuracy with which the resulting policies are able to capture our subjects' preferences under a variety of assumptions about the sensitivity of the information and user-burden tolerance. One practical implication of our results is that today's location-sharing applications may have failed to gain much traction due to their limited privacy settings, as they appear to be ineffective at capturing the preferences revealed by our study.

References

[1]

Barkhuus L, Brown B, Bell M, Hall M, Sherwood S, Chalmers M (2008) From awareness to repartee: sharing location within social groups. In: Proceedings of the conference on human factors in computing systems (CHI)

[2]

Barkhuus L, Dey A (2003) Location-based services for mobile telephony: a study of users' privacy concerns. In: Proceedings of the international conference on human-computer interaction (INTERACT)

[3]

Benisch M, Sadeh N, Sandholm T (2008) A theory of expressiveness in mechanisms. In: Proceedings of the national conference on artificial intelligence (AAAI)

[4]

Benisch M, Sadeh N, Sandholm T (2009) Methodology for designing reasonably expressive mechanisms with application to ad auctions. In: Proceedings of the international joint conference on artificial intelligence (IJCAI)

[5]

Burghardt T, Buchmann E, Müller J, Böhm K (2009) Understanding user preferences and awareness: privacy mechanisms in location-based services. In: Proceedings of the onthemove conferences (OTM)

[6]

Connelly K, Khalil A, Liu Y (2007) Do I do what I say? Observed versus stated privacy preferences. In: Proceedings of the international conference on human-computer interaction (INTERACT)

[7]

Consolovo S, Smith I, Matthews T, LaMarca A, Tabert J, Powledge P (2005) Location disclosure to social relations: why, when, and what people want to share. In: Proceedings of the conference on human factors in computing systems (CHI)

[8]

Cornwell J, Fette I, Hsieh G, Prabaker M, Rao J, Tang K, Vaniea K, Bauer L, Cranor L, Hong J, McLaren B, Reiter M, Sadeh N (2007) User-controllable security and privacy for pervasive computing. In: Proceedings of the workshop on mobile computing systems and applications

[9]

Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779-782

[10]

K Group (2009) BIA's The Kelsey Group Forecasts U.S. mobile local search advertising revenues to reach $1.3B in 2013. http://www.kelseygroup.com/press

[11]

Hightower J, LaMarca A, Smith IE (2006) Practical lessons from place lab. IEEE Pervasive Comput 5(3):32-39

[12]

Huang S, Proulx F, Ratti C (2007) iFIND: a Peer-to-Peer application for real-time location monitoring on the MIT campus. In: International conference on computers in urban planning and urban management (CUPUM)

[13]

Iachello G, Smith I, Consolovo S, Abowd G, Hughes J, Howard J, Potter F, Scott J, Sohn T, Hightower J, LaMarca A (2005) Control, deception, and communication: evaluating the deployment of a location-enhanced messaging service. In: Proceedings of the international conference on ubiquitous computing (UbiComp)

[14]

Kelley PG, Benisch M, Sadeh N, Cranor LF (2010) When are users comfortable sharing locations with advertisers? Technical Report CMU-ISR-10-126, Carnegie Mellon University

[15]

Lederer S, Mankoff J, Dey AK (2003) Who wants to know what when? Privacy preference determinants in ubiquitous computing. In: Proceedings of the conference on human factors in computing systems (CHI)

[16]

Mazurek M, Arsenault J, Bresee J, Gupta N, Ion I, Johns C, Lee D, Liang Y, Olsen J, Salmon B, Shay R, Vaniea K, Bauer L, Cranor L, Ganger G, Reiter M (2010) Access control for home data sharing: attitudes, needs and practices. In: Proceedings of the conference on human factors in computing systems (CHI)

[17]

Miller CC, Wortham J (2010) Technology aside, most people still decline to be located. http://www.nytimes.com/2010/08/30/ technology/30location.html

[18]

Patil S, Lai J (2005) Who gets to know what when: configuring privacy permissions in an awareness application. In: Proceedings of the conference on human factors in computing systems (CHI)

[19]

Sadeh N, Gandon F, Kwon OB (2006) Ambient intelligence: the MyCampus experience. In: Vasilakos T, Pedrycz W (eds) Ambient intelligence and pervasive computing. ArTech House, Norwood

[20]

Sadeh N, Hong J, Cranor L, Fette I, Kelley P, Prabaker M, Rao J (2009) Understanding and capturing people's privacy policies in a mobile social networking application. J Pers Ubiquit Comput 13(6):401-412

[21]

Simon HA (1957) Models of man. Wiley, New York

[22]

Smith I, Consolovo S, LaMarca A, Hightower J, Scott J, Sohn T, Hughes J, Iachello G, Abowd G (2005) Social disclosure of place: from location technology to communication practices. In: Lecture notes in computer science: pervasive computing, pp 134-151

[23]

Toch E, Cranshaw J, Drielsma PH, Tsai JY, Kelley PG, Springfield J, Cranor L, Hong J, Sadeh N (2010) Empirical models of privacy in location sharing. In: International conference on Ubiquitous Computing (UbiComp), Copenhagen, Denmark

[24]

Tsai J, Kelley P, Cranor L, Sadeh N (2009) Location-sharing technologies: privacy risks and controls. In: Research conference on communication, information and internet policy (TPRC)

[25]

Tsai J, Kelley P, Drielsma PH, Cranor LF, Hong J, Sadeh N (2009) Who's viewed you? The impact of feedback in a mobilelocation system. In: Proceedings of the conference on human factors in computing systems (CHI)

[26]

Wang Y, Lin J, Annavaram M, Jacobson QA, Hong J, Krishnamachari B, Sadeh N (2009) A framework of energy efficient mobile sensing for automatic user state recognition. In: International conference on mobile systems, applications, and services (MobiSys)

[27]

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

Cited By

Islami LKitkowska AFischer-Hübner S(2024)Inter-regional Lens on the Privacy Preferences of Drivers for ITS and Future VANETsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641997(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641997
Guerra MScalabrino SFasano FOliveto R(2023)An Empirical Study on the Effectiveness of Privacy IndicatorsIEEE Transactions on Software Engineering10.1109/TSE.2023.330839249:10(4610-4623)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3308392
Filipczuk DBaarslag TGerding Eschraefel m(2022)Automated privacy negotiations with preference uncertaintyAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09579-136:2Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10458-022-09579-1
Show More Cited By

Index Terms

Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs

Recommendations

A comparative study of location-sharing privacy preferences in the United States and China

While prior studies have provided us with an initial understanding of people's location-sharing privacy preferences, they have been limited to Western countries and have not investigated the impact of the granularity of location disclosures on people's ...
Location sharing privacy preference: analysis and personalized recommendation
IUI '14: Proceedings of the 19th international conference on Intelligent User Interfaces

Location-based systems are becoming more popular with the explosive growth in popularity of smart phones. However, the user adoption of these systems is hindered by growing user concerns about privacy. To design better location-based systems that ...
Examining Privacy Concern in Social-Driven Location Sharing: An Empirical Study on Chinese Popular SNSs
COMPSAC '13: Proceedings of the 2013 IEEE 37th Annual Computer Software and Applications Conference

Sharing location in SNSs has witnessed rapid development in recent years. Privacy is undoubtedly a barrier to the adoption of such location sharing services. In this paper, we investigate what factors affect users' privacy concerns and how privacy ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Personal and Ubiquitous Computing

Personal and Ubiquitous Computing Volume 15, Issue 7

October 2011

112 pages

ISSN:1617-4909

Issue’s Table of Contents

Copyright © Copyright © 2011 Springer-Verlag London Limited.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2011

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

49
Total Citations
View Citations
296
Total Downloads

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

Reflects downloads up to 16 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Islami LKitkowska AFischer-Hübner S(2024)Inter-regional Lens on the Privacy Preferences of Drivers for ITS and Future VANETsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641997(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641997
Guerra MScalabrino SFasano FOliveto R(2023)An Empirical Study on the Effectiveness of Privacy IndicatorsIEEE Transactions on Software Engineering10.1109/TSE.2023.330839249:10(4610-4623)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3308392
Filipczuk DBaarslag TGerding Eschraefel m(2022)Automated privacy negotiations with preference uncertaintyAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09579-136:2Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10458-022-09579-1
Wu TChien YLee HChang YKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)IM Receptivity and Presentation-type Preferences among Users of a Mobile App with Automated Receptivity-status AdjustmentProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445209(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445209
Feng YYao YSadeh NKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)A Design Space for Privacy Choices: Towards Meaningful Privacy Control in the Internet of ThingsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445148(1-16)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445148
Fan LGunja SBouros PMagdy ARenz MSarwat MZüfle A(2020)Geopriv4jProceedings of the Sixth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/3403896.3403968(1-6)Online publication date: 14-Jun-2020
https://dl.acm.org/doi/10.1145/3403896.3403968
Li YKobsa A(2020)Context and Privacy Concerns in Friend Request DecisionsJournal of the Association for Information Science and Technology10.1002/asi.2429171:6(632-643)Online publication date: 7-May-2020
https://dl.acm.org/doi/10.1002/asi.24291
Wu HZhang HCui LWang X(2018)A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for RecommendationSecurity and Communication Networks10.1155/2018/27903732018Online publication date: 4-Feb-2018
https://dl.acm.org/doi/10.1155/2018/2790373
Emami Naeini PDegeling MBauer LChow RCranor LHaghighat MPatterson H(2018)The Influence of Friends and Experts on Privacy Decision Making in IoT ScenariosProceedings of the ACM on Human-Computer Interaction10.1145/32743172:CSCW(1-26)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1145/3274317
Wilkinson DNamara MBadillo-Urquiola KWisniewski PKnijnenburg BPage XToch ERomano-Bergstrom JMandryk RHancock MPerry MCox A(2018)Moving Beyond a "one-size fits all"Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3170427.3170617(1-8)Online publication date: 20-Apr-2018
https://dl.acm.org/doi/10.1145/3170427.3170617
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents