An Overview of Location Privacy for Mobile Computing

1. An Overview of Location Privacy for Mobile Computing Jason Hong jasonh@cs.cmu.edu

2. Ubiquity of Location-Enabled Devices [Berg Insight ‘10] 2009: 150 million GPS-equipped phones shipped 2014: 770 million GPS-equipped phones expected to ship (~ 5x increase!) Future: Every mobile device will be location-enabled 2

3. Location-Based Services Growing 3

4. Lots of Location-Based Services Claims over 5 million users 4

5. Potential Benefits of Location Okayness checking Micro-coordination Games Exploring a city Info retrieval / filtering Ex. geotagging of photos Activity recognition Ex. walking, driving, bus Improving trust Co-locations to infer tie strength and trust

6. Potential Risks Little sister Undesired social obligations Wrong inferences Over-monitoring by employers Failing to address accidents and legitimate concerns could blunt adoption of a promising technology

7. Protecting Location Privacy System architecture How you get location Where and how data stored and used User interface and policies When is it shared How is it displayed User studies How do people manage in practice

8. Protecting Location Privacy System architecture How you get location Where and how data stored and used User interface and policies When is it shared How is it displayed User studies How do people manage in practice

9. How You Get and Use Location Some location-based content,even if old, still useful Different time-to-live Real-time Traffic, Parking spots, Friend Finder Daily Weather, Social events, Coupons Weekly Movie schedules, Ads, Yelp! Monthly Geocaches, Bus schedules Yearly Maps, Store locations, Restaurants Shah Amini et al, Caché: Caching Location-Enhanced Content to Improve User Privacy. (Under Review)

10. How You Get and Use Location Pre-fetch all the content you might need for a geographic area in advance SELECT * from DB where City=‘Pittsburgh’ Then, use it locally on your device only We assume that you determine your location locally using WiFi or GPS So a content provider would only know you are in Pittsburgh

11. Feasibility of Pre-Fetching Are people’s mobility patterns regular? Pre-fetching useful only if we can predict where people will be Locaccino: Top 20 of 4000, 460k traces Place naming: 26 people, 118k traces For each person, 5mi radius around two most common places (home + work) accounts for what % of mobility data?

12. Feasibility of Pre-Fetching Home 5mi Work

13. Feasibility of Pre-Fetching Radius Locaccino Place Naming 5mi 86% 79% 10mi 87% 84% 15mi 87% 86%

14. Feasibility of Pre-Fetching Content doesn’t change that often Average amount of change per day (over 5 months) Downloading it doesn’t take long NYC has 250k POI = 100MB, 65MB for map

15. Caché Toolkit Android background service for apps Apps modified to make requests to service User specifies home and work locations Caché service pre-fetches content in background when plugged in and WiFi Caché also gets content for your region if you spend night there

16. Protecting Location Privacy System architecture How you get location Where and how data stored and used User interface and policies When is it shared How is it displayed User studies How do people manage in practice

17. Why People Use Foursquare Started in Mar 2009, 5 million users After two decades of research, finally a LBS beyond navigation Large graveyard of location apps Critical mass of devices and developers Opportunity to study value proposition and how people manage privacy Janne Lindqvist et al, I’m the Mayor of My House: Examining Why People Use a Social-Driven Location Sharing Application, CHI 2011

18. What is Foursquare? “Foursquare is a mobile application that makes cities easier to use and more interesting to explore. It is a friend-finder, a social city guide and a game that challenges users to experience new things, and rewards them for doing so. Foursquare lets users "check in" to a place when they're there, tell friends where they are and track the history of where they've been and who they've been there with.”

19. How Does Foursquare Work? Check-in See list of nearby places Manually select a place “Off the grid” option Can create new places Facebook + Twitter too Can see check-ins of friends, plus who else is at your location

20. How Does Foursquare Work?

21. How Does Foursquare Work? Leave tips for others

22. How Does Foursquare Work? Earn badges for activities

23. How Does Foursquare Work? Become mayor of a place if youhave most check-ins in past 60 days Wean Hall http://foursquare.com/venue/209221 Gates http://foursquare.com/venue/174205

24. News of the Weird People fighting to be mayors of a place One pair eventually got engaged Some people mayor of 30+ places Some businesses offering discounts to mayors

25. Three-Part Study of Foursquare Why do people use foursquare? How do they manage privacy concerns? Surprising uses? Interviews with early adopters of LBS (N=6) First survey to understand range of uses of foursquare (N=18) Second survey to understand details of use, especially privacy (N=219)

26. Why People Check-In Principal components analysis based on survey data See paper for details Foursquare’s mission statement quite accurate Fun (mayorships, badges) Keep in touch with friends Explore a city Personal history

27. Privacy IssuesWhy people don’t check-in Presentation of Self issues Didn’t want to be seen in McDonalds or fast food Boring places, or at Doctor’s Didn’t want to spam friends Facebook and Twitter Didn’t want to reveal location of home Tension: “Home” to signal availability Tension: Some checked-in everywhere

28. Privacy Issues

29. Privacy Issues Surprisingly few concerns about stalkers Only 9/219 participants (but early adopters) Checking in when leaving (safety) Surprising use, 29 people said they did this 71 people (32%) used for okayness checking Over half of participants had a stranger on their friends list Want to know where interesting people go Perceived like Twitter followers Suggests separating Friends from friends

30. Protecting Location Privacy System architecture How you get location Where and how data stored and used User interface and policies When is it shared How is it displayed User studies How do people manage in practice

31. Sharing One’s Location Place naming “Hey mom, I am at 55.66N12.59E.” vs “Home” User study + machine learning to model how people name places Semantic: business, function, personal Geographic: city, street, building Jialiu Lin et al, Modeling People’s Place Naming Preferences in Location Sharing, Ubicomp 2010

32. Sharing One’s Location Location abstractions share precise location (GPS) & max social benefits share nothing & no social benefits

33. Sharing One’s Location Location abstractions use location abstractions to scaffold privacy concerns share precise location (GPS) & max social benefits share nothing & no social benefits

34. Sharing One’s Location Location abstractions

35. Sharing One’s Location Place entropy

36. Understanding Human Behavior at Large Scales Capabilities of today’s mobile devices Location, sound, proximity, motion Call logs, SMS logs, pictures We can now analyze real-world social networks and human behaviors at unprecedented fidelity and scale 2.8m location sightings of 489 volunteers in Pittsburgh

37. Insert graph here Describe entropy

38. Early Results Can predict Facebook friendships based on co-location patterns 67 different features Intensity and Duration Location diversity (entropy) Mobility Specificity (TF-IDF) Graph structure (mutual neighbors, overlap) 92% accuracy in predicting friend/not Justin Cranshaw et al, Bridging the Gap Between Physical Location and Online Social Networks, Ubicomp 2010

39. Using features such a location entropy significantly improves performance over shallow features such as number of co-locations 39

40. Without intensity Full model Number of co-locations Intensity features 40

41. Early Results Can predict number of friends based on mobility patterns People who go out often, on weekends, and to high entropy places tend to have more friends (Didn’t check age though) Justin Cranshaw et al, Bridging the Gap Between Physical Location and Online Social Networks, Ubicomp 2010

42. Entropy Related to Location Privacy

43. Ongoing Work Managing geotagged photos Enhanced social graph Understanding real-world human behavior at large scales

44. Managing Geotagged Photos 4.3% Flickr photos, 3% YouTube, 1% Craigslist photos geotagged Idea: Use place entropy to differentiate between public / private But need to radically scale up entropy 2.8m sightings, 489 volunteers, N years Wired Magazine story

45. Calculating Entropy from Flickr

46. Foursquare Check-in Data Viz of 566k check-ins in NYC

47. Enhanced Social Graph Family, friends, co-workers, acquaintances all mixed together Gay friends and 12yo swimmers Family friends and high school friends Friends and boss My personal use

48. Enhanced Social Graph Create a more sophisticated graph that captures tie strength and relationship Take call data, SMS, FB use, co-locations More appropriate sharing

49. Understanding Human Behavior at Large Scales What does me going to a placesay about me and that place? Scale up to thousands of people, what does it say about people in a city?

50. Understanding Human Behavior at Large Scales Utility for individuals Predict onset of depression Infer physical decline Predict personality type Utility for groups Architecture and urban design Use of public resources (e.g. buses) Traffic Behavioral Inventory (TBI) Ride-sharing estimates What do Pittsburgher’s do? What do Chinese people in Pittsburgh do?