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Empirical Models of Privacy in Location Sharing, at Ubicomp2010
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Empirical Models of Privacy in Location Sharing, at Ubicomp2010


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Authors are Eran Toch, Justin Cranshaw, Paul Hankes-Drielsma, Janice Y. Tsai, Patrick Gage Kelley, James Springfield, Lorrie Cranor, Jason Hong, Norman Sadeh …

Authors are Eran Toch, Justin Cranshaw, Paul Hankes-Drielsma, Janice Y. Tsai, Patrick Gage Kelley, James Springfield, Lorrie Cranor, Jason Hong, Norman Sadeh

Published in: Technology, Economy & Finance
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  • The one measure that was significant was location entropy
  • We showed unique locations - 500 meters away from other places And only locations in which they were 5 minutes or more
  • Number of observations has a bias in in homes. If users visits their home a lot, then they will have high entropy.
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    • 1. Empirical Models of Privacy in Location Sharing Eran Toch, Justin Cranshaw, Paul Hankes-Drielsma, Janice Y. Tsai, Patrick Gage Kelley, James Springfield, Lorrie Cranor, Jason Hong, Norman Sadeh Carnegie Mellon
    • 2. (1) Motivation
    • 3. Ubicomp 2010 Carnegie Mellon Motivation
    • 4. 4Ubicomp 2010 Carnegie Mellon Privacy ‣ Location sharing applications can reveal sensitive locations (e.g., home,) the activity of the user, social encounters etc... ‣ Privacy is a major concern that may limit adoption (Tsai et al. 2009.) by Frank Groeneveld, Barry Borsboom and Boy van Amstel.
    • 5. Ubicomp 2010 Carnegie Mellon Background ‣ Privacy ‣ Khalil and Connelly (2006) ‣ Anthony et al. (2007) ‣ Benisch et al. (2010) Location and Mobility ‣ Eagle et al. (2006) ‣ Gonz´alez et al. (2008) ‣ Mancini et al. (2009) ‣ Cranshaw et al., 2010
    • 6. Our question: What are the privacy preferences associated with locations and mobility patterns?
    • 7. 7Ubicomp 2010 Carnegie Mellon Agenda ‣ Locaccino ‣ Study ‣ Results ‣ Conclusions
    • 8. (2) Locaccino
    • 9. Ubicomp 2010 Carnegie Mellon Locaccino ‣ Location sharing application ‣ Expressive privacy controls ‣ Background location tracking ‣ Research framework
    • 10. 10Ubicomp 2010 Carnegie Mellon Locators ‣ Background location reporting every 2-10 minutes, depending on movement ‣ On laptops: Location WiFi positioning by Skyhook ‣ On smartphones: WiFi positioning + GPS For Mac and Windows
    • 11. Ubicomp 2010 Carnegie Mellon Setting Privacy Policy
    • 12. Ubicomp 2010 Carnegie Mellon Requesting Locations
    • 13. (3) Study
    • 14. Ubicomp 2010 Carnegie Mellon Study ‣ 28 primary participants were recruited using flyers scattered around the Carnegie Mellon Campus and mailing list posting. They were compensated at $30 + data plan. ‣ 373 secondary participants had joined by invitation of primary participants. They were not compensated. ‣ 230 of them installed a locator, and were requested by other participants. 1. Answering Entrance Survey 3. Installing locator 4. Setting up privacy policy 5. Inviting friends 3. Using Locaccino 4. Answering Place Survey + Exit Survey 2. Randomly assigned a locator
    • 15. Ubicomp 2010 Carnegie Mellon Population and Limitation ‣ All participants are from the university community. ‣ 17 graduate students, 9 undergraduate students and 2 staff members. ‣ The study was conducted in a single city (Pittsburgh.) ‣ And in the course of a single summer month.
    • 16. (4) Results
    • 17. Ubicomp 2010 Carnegie Mellon Location Entropy ‣ Entropy is a measure for the diversity of visitors to a place (Cranshaw et al., 2010) ‣ Borrowed from bio-diversity, it assigns high values to places visited by many users in equal proportions. ‣ Let p(u,l) be the observations of a user u in a location l. Entropy is defined as: High entropy (5+) Medium entropy (1-5) Low entropy (1) Locations are defined based a 100m radius
    • 18. Ubicomp 2010 Carnegie Mellon Place Survey
    • 19. Ubicomp 2010 Carnegie Mellon Entropy vs. Comfort in sharing locations Users were more comfortable sharing high entropy locations. ANOVA, friends: F=5.46 p=0.02, distant relations: F = 15.57 p=0.001 The correlation is stronger for distant social relations than with close social relations
    • 20. Ubicomp 2010 Carnegie Mellon Sharing by Place Type Tags were grouped by a team of 3 judges to 8 categories For distant relations
    • 21. Ubicomp 2010 Carnegie Mellon Privacy and Mobility • Visible mobility is correlated with the number of request for the user (ANOVA: F = 14.713 p = 0.00079) ‣ High mobility users were requested twice as much as low mobility users. ‣ Number of friends and the users’ activity are non significant. High mobility users Low mobility users Visible mobility Number of unique daily locations Median: 3.4
    • 22. Ubicomp 2010 Carnegie Mellon Requests over time The request rate for high mobility users increased twofold over the course of the study
    • 23. Ubicomp 2010 Carnegie Mellon Privacy and Mobility Item ANOVA F ANOVA P-value Expressiveness (number of policy restrictions) 5.63 0.025 Number of privacy policy updates 10.75 0.0028 Correlation between visible mobility and privacy properties High mobility users were 4 times as likely to use location restrictions and 7 times more likely to use time restrictions
    • 24. 24Ubicomp 2010 Carnegie Mellon Rule Examples
    • 25. Ubicomp 2010 Carnegie Mellon Survey Results Item Average ANOVA F ANOVA P-value Overall Usefulness 4.74 4.54 0.043 Friends rules usefulness 5.48 4.68 0.04 Time rules usefulness 4.74 5.14 0.03 Location rules usefulness 5.14 4.15 0.052 ‣Correlation between visible mobility and survey results 7-point Likert (1 stands for not useful and 7 for very useful)
    • 26. (4) Conclusions
    • 27. Ubicomp 2010 Carnegie Mellon Conclusions ‣ Some privacy preferences can be predicted by location entropy and mobility. ‣ Enhancing location sharing: by suggesting helpful defaults, checking-in in high entropy places etc. ‣ Establishing privacy sensitive location reporting for location aware systems. ‣ Other fields? Is entropy related to other phenomena? Check Session VII ‣ Lots of future work...
    • 28. Thank you More info: Carnegie Mellon Locaccino demo - tomorrow’s lunch
    • 29. Ubicomp 2010 Carnegie Mellon Location Privacy Preferences ‣Which measure best predicts the location privacy preferences? ANOVA p-value Measure friends and family distant relations Number of unique visitors 0.48 0.3 Number of observations 0.17 0.001 User’s visits to the location 0.98 0.22 Location entropy 0.02 0.001
    • 30. 30Ubicomp 2010 Carnegie Mellon Statistics Item Average Number of friends 12.86 Number of location observations 1,417,095