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Empirical Models of Privacy in Location Sharing

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Recently, I presented our paper "Empirical models of privacy in location sharing" (PDF) in Ucibomp - a great conference about mobile computing. The idea behind the paper is to study what type of ...

Recently, I presented our paper "Empirical models of privacy in location sharing" (PDF) in Ucibomp - a great conference about mobile computing. The idea behind the paper is to study what type of privacy preferences people assign to distinct places in location sharing scenarios (e.g., when using applications such as Foursquare, Facebook Places and Google Latitude). In order to study this question, we asked 28 people to use our location sharing application, Locaccino, with their actual friends for a month. We found out that people are more likely to share places with high entropy: places which are visited frequently by a diverse population. Entropy is a super-easy method for predicting which places will be shared. Also, we have seen that users who visit lots of low entropy places are more likely to have more privacy concerns and more restrictive privacy preferences.

You can find more information about the paper here: http://bit.ly/aRLqje

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  • The one measure that was significant was location entropy <br />
  • The one measure that was significant was location entropy <br />
  • The one measure that was significant was location entropy <br />
  • The one measure that was significant was location entropy <br />
  • We showed unique locations - 500 meters away from other places <br /> And only locations in which they were 5 minutes or more <br />
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  • Number of observations has a bias in in homes. If users visits their home a lot, then they will have high entropy. <br />
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Empirical Models of Privacy in Location Sharing Presentation Transcript

  • 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. Motivation Ubicomp 2010 Carnegie Mellon
  • 4. Motivation Ubicomp 2010 Carnegie Mellon
  • 5. Motivation Ubicomp 2010 Carnegie Mellon
  • 6. Motivation Ubicomp 2010 Carnegie Mellon
  • 7. Motivation Ubicomp 2010 Carnegie Mellon
  • 8. Motivation Ubicomp 2010 Carnegie Mellon
  • 9. Privacy by Frank Groeneveld, Barry Borsboom and Boy van Amstel. ‣ 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.) Ubicomp 2010 Carnegie Mellon 4
  • 10. Background Privacy Location and Mobility ‣ Khalil and Connelly ‣ Eagle et al. (2006) (2006) ‣ Gonz´alez et al. (2008) ‣ Anthony et al. (2007) ‣ Mancini et al. (2009) ‣ Benisch et al. (2010) ‣ Cranshaw et al., 2010 Ubicomp 2010 Carnegie Mellon
  • 11. Our question: What are the privacy preferences associated with locations and mobility patterns?
  • 12. Agenda ‣ Locaccino ‣ Study ‣ Results ‣ Conclusions Ubicomp 2010 Carnegie Mellon 7
  • 13. (2) Locaccino
  • 14. Locaccino ‣ Location sharing application ‣ Expressive privacy controls ‣ Background location tracking ‣ Research framework Ubicomp 2010 Carnegie Mellon
  • 15. Locators For Mac and Windows ‣ Background location reporting every 2-10 minutes, depending on movement ‣ On laptops: Location WiFi positioning by Skyhook ‣ On smartphones: WiFi positioning + GPS Ubicomp 2010 Carnegie Mellon 10
  • 16. Locators For Mac and Windows ‣ Background location reporting every 2-10 minutes, depending on movement ‣ On laptops: Location WiFi positioning by Skyhook ‣ On smartphones: WiFi positioning + GPS Ubicomp 2010 Carnegie Mellon 10
  • 17. Setting Privacy Policy Ubicomp 2010 Carnegie Mellon
  • 18. Requesting Locations Ubicomp 2010 Carnegie Mellon
  • 19. Requesting Locations NOKIA N95 240 X 320 Ubicomp 2010 Carnegie Mellon
  • 20. (3) Study
  • 21. 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. Ubicomp 2010 Carnegie Mellon
  • 22. 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. 1. Answering 2. Randomly 3. Installing locator 3. Using 4. Answering Entrance assigned a 4. Setting up privacy Locaccino Place Survey Survey locator policy + Exit Survey 5. Inviting friends Ubicomp 2010 Carnegie Mellon
  • 23. 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. 1. Answering 2. Randomly 3. Installing locator 3. Using 4. Answering Entrance assigned a 4. Setting up privacy Locaccino Place Survey Survey locator policy + Exit Survey 5. Inviting friends ‣ 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. Ubicomp 2010 Carnegie Mellon
  • 24. 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. Ubicomp 2010 Carnegie Mellon
  • 25. (4) Results
  • 26. Location Entropy High entropy (5+) Medium entropy (1-5) Low entropy (1) Locations are defined based a 100m radius Ubicomp 2010 Carnegie Mellon
  • 27. Location Entropy ‣ Entropy is a measure for the diversity of visitors to a place (Cranshaw et al., 2010) High entropy (5+) Medium entropy (1-5) Low entropy (1) Locations are defined based a 100m radius Ubicomp 2010 Carnegie Mellon
  • 28. 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. High entropy (5+) Medium entropy (1-5) Low entropy (1) Locations are defined based a 100m radius Ubicomp 2010 Carnegie Mellon
  • 29. 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. High entropy (5+) Medium entropy (1-5) ‣ Let p(u,l) be the observations Low entropy (1) of a user u in a location l. Entropy is defined as: Locations are defined based a 100m radius Ubicomp 2010 Carnegie Mellon
  • 30. Place Survey Ubicomp 2010 Carnegie Mellon
  • 31. 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 Ubicomp 2010 Carnegie Mellon
  • 32. Sharing by Place Type For distant relations Tags were grouped by a team of 3 judges to 8 categories Ubicomp 2010 Carnegie Mellon
  • 33. Privacy and Mobility Visible mobility Number of unique daily locations Low High mobility Median: 3.4 mobility users users Ubicomp 2010 Carnegie Mellon
  • 34. Privacy and Mobility ‣ Visible mobility is Visible mobility correlated with the Number of unique daily locations number of request for the user (ANOVA: F = 14.713 p = 0.00079) Low High mobility Median: 3.4 mobility ‣ High mobility users were users users requested twice as much as low mobility users. ‣ Number of friends and the users’ activity are non significant. Ubicomp 2010 Carnegie Mellon
  • 35. Requests over time The request rate for high mobility users increased Ubicomp 2010 twofold over the course of the study Carnegie Mellon
  • 36. Privacy and Mobility High mobility users were 4 times as likely to use location restrictions and 7 times more likely to use time restrictions Item ANOVA F ANOVA P-value Expressiveness (number 5.63 0.025 of policy restrictions) Number of privacy policy 10.75 0.0028 updates Correlation between visible mobility and privacy properties Ubicomp 2010 Carnegie Mellon
  • 37. Rule Examples Ubicomp 2010 Carnegie Mellon 24
  • 38. Survey Results Correlation between visible mobility and 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 5.14 4.15 0.052 usefulness 7-point Likert (1 stands for not useful and 7 for very useful) Ubicomp 2010 Carnegie Mellon
  • 39. (4) Conclusions
  • 40. Conclusions Ubicomp 2010 Carnegie Mellon
  • 41. Conclusions ‣ Some privacy preferences can be predicted by location entropy and mobility. Ubicomp 2010 Carnegie Mellon
  • 42. 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. Ubicomp 2010 Carnegie Mellon
  • 43. 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. Ubicomp 2010 Carnegie Mellon
  • 44. 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 Ubicomp 2010 Carnegie Mellon
  • 45. 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... Ubicomp 2010 Carnegie Mellon
  • 46. Thank you More info: http://www.cs.cmu.edu/~eran/ Locaccino demo - tomorrow’s lunch Carnegie Mellon
  • 47. Location Privacy Preferences Which measure best predicts the location privacy preferences? ANOVA p-value Measure friends and distant relations family 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 Ubicomp 2010 Carnegie Mellon
  • 48. Statistics Item Average Number of friends 12.86 Number of location observations 1,417,095 Ubicomp 2010 Carnegie Mellon 30