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Location Privacy for Mobile Computing, Cylab Talk on Feb 2011
 

Location Privacy for Mobile Computing, Cylab Talk on Feb 2011

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A talk I gave for Cylab in Feb 2011 on location privacy, summarizing some of my group's work in this area. I discuss some system architectures for location-based content (using pre-fetching and ...

A talk I gave for Cylab in Feb 2011 on location privacy, summarizing some of my group's work in this area. I discuss some system architectures for location-based content (using pre-fetching and caching to manage privacy), why people use foursquare, and some empirical work on location sharing.

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  • Back in 1989, Magellan released the first commercial handheld GPS device. Now fast-forward twenty years and today we have highly accurate positioning technology, like GPS, readily available in mobile phones. Just last year, approximately 150 million GPS-equipped phones were shipped and, over the next few years, this number is expected to continue growing.
  • This trend has made location-aware technology much more accessible than before. And the result is clear: more location-based services are being deployed. Some of these are what I would refer to as “location-aware”, which is to say that they simple use your location in order to provide some kind of lookup service. Services like Yelp and Where would fall under this category. However, there is an emerging class of services which I refer to as “social location-sharing applications”.
  • Foursquare is first really widely adopted lbs that isn’t navigation
  • approach and style: hci / systems / machine learning how you get location placelab where and how stored cache when shared (rules - who when where activity) locaccino / mobile messaging / social sharing / entropy how displayed passive-active / place naming how used foursquare study
  • approach and style: hci / systems / machine learning how you get location placelab where and how stored cache when shared (rules - who when where activity) locaccino / mobile messaging / social sharing / entropy how displayed passive-active / place naming how used foursquare study
  • Tor issues: performance hit, potential issues if poor network speed, and doesn’t work well for paid accounts
  • approach and style: hci / systems / machine learning how you get location placelab where and how stored cache when shared (rules - who when where activity) locaccino / mobile messaging / social sharing / entropy how displayed passive-active / place naming how used foursquare study
  • http://www.4squarebadges.com/foursquare-badge-list/
  • http://www.4squarebadges.com/foursquare-badge-list/
  • Wean Hall http://foursquare.com/venue/209221 Gates http://foursquare.com/venue/174205
  • http://www.nytimes.com/2010/08/19/fashion/19foursquare.html
  • approach and style: hci / systems / machine learning how you get location placelab where and how stored cache when shared (rules - who when where activity) locaccino / mobile messaging / social sharing / entropy how displayed passive-active / place naming how used foursquare study
  • Entropy related to location privacy Fewer concerns in “public” places
  • What this means is, just looking at very obvious properties of the co-locations histories doesn't really tell you very much. Also, notice most of the performance boost is at low levels of recall. so if you want to build a high-precision classifier this is the best approach. Really there are two stories here. first it's that the intensity features do not really provide much of a gain over just looking at the number of locations, especially at high recall levels. Second, is that location based features significantly improves performance. This validates that these are clearly good things to look at when you're analyzing this kind of data
  • What this means is, just looking at very obvious properties of the co-locations histories doesn't really tell you very much. Also, notice most of the performance boost is at low levels of recall. so if you want to build a high-precision classifier this is the best approach. Really there are two stories here. First it's that the intensity features (time spent co-located) do not really provide much of a gain over just looking at the number of locations, especially at high recall levels. Second, is that location based features (ie entropy) significantly improves performance. This validates that these are clearly good things to look at when you're analyzing this kind of data
  • Entropy related to location privacy Fewer concerns in “public” places
  • Burst, Normalcy, Effort, RepeatVisit, TimeSpent, etc
  • http://www.wired.com/gadgets/wireless/magazine/17-02/lp_guineapig Friedland, Gerald, and Robin Sommer. 2010. Cybercasing the Joint: On the Privacy Implications of Geo-Tagging. In 5th Usenix Hot Topics in Security Workshop (HotSec2010) . http://www.usenix.org/events/hotsec10/tech/full_papers/Friedland.pdf.

Location Privacy for Mobile Computing, Cylab Talk on Feb 2011 Location Privacy for Mobile Computing, Cylab Talk on Feb 2011 Presentation Transcript

  • ©2009CarnegieMellonUniversity:1 Location Privacy for Mobile Computing Jason Hong jasonh@cs.cmu.edu
  • ©2011CarnegieMellonUniversity:2 Ubiquity of Location-Enabled Devices •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 (GPS or WiFi) 2 [Berg Insight ‘10]
  • ©2011CarnegieMellonUniversity:3 Location-Based Services Growing 3
  • ©2011CarnegieMellonUniversity:4 Lots of Location-Based Services 4 Claims over 5 million users
  • ©2011CarnegieMellonUniversity:5 Potential Benefits of Location • Okayness checking • Micro-coordination • Games – Exploring a city • Info retrieval / filtering – Ex. geotagging photos, tweets • Activity recognition – Ex. walking, driving, bus • Improving trust – Co-locations to infer tie strength and trust
  • ©2011CarnegieMellonUniversity: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
  • ©2011CarnegieMellonUniversity:7 Our Work in Location Privacy • System architectures – Architectures for location-based content – Estimating how many people in a location • User studies – Why do people use foursquare? – Sharing location in China vs US • User interfaces and policies – How to help people create policies? – How do people name places? – Large scale analysis of location traces
  • ©2011CarnegieMellonUniversity:8 Talk Outline • System architectures – Architectures for location-based content – Estimating how many people in a location • User studies – Why do people use foursquare? – Sharing location in China vs US • User interfaces and policies – How to help people create policies? – How do people name places? – Large scale analysis of location traces
  • ©2011CarnegieMellonUniversity:9 Location-based Content • Some location-based content, even if old, still useful • Different time-to-live Amini et al, Caché: Caching Location-Enhanced Content to Improve User Privacy. (Under Review) Real-time Daily Weekly Monthly Yearly Traffic, Parking spots, Friend Finder Weather, Social events, Coupons Movie schedules, Ads, Yelp! Geocaches, Bus schedules Maps, Store locations, Restaurants
  • ©2011CarnegieMellonUniversity:10 Caching Location-based Content • 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
  • ©2011CarnegieMellonUniversity: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 people, 460k traces – Place naming: 26 people, 118k traces • For each person, take a 5mi radius around two most common places (home + work) – What % of all mobility data does this account for?
  • ©2011CarnegieMellonUniversity:12 Feasibility of Pre-Fetching 5mi Work Home
  • ©2011CarnegieMellonUniversity:13 Feasibility of Pre-Fetching Radius 5mi 10mi 15mi Locaccino 86% 87% 87% Place Naming 79% 84% 86%
  • ©2011CarnegieMellonUniversity: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
  • ©2011CarnegieMellonUniversity: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
  • ©2011CarnegieMellonUniversity:16 Caché Discussion • Doesn’t work for time-sensitive content • Tor anonymizing servers – Performance hit for mobile devices – Tor not useful for named accounts • Better content distribution models • Still need user studies of effectiveness in practice
  • ©2011CarnegieMellonUniversity:17 Talk Outline • System architectures – Architectures for location-based content • User studies – Why do people use foursquare? • User interfaces and policies – Large scale analysis of location traces
  • ©2011CarnegieMellonUniversity:18 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 Lindqvist et al, I’m the Mayor of My House: Examining Why People Use a Social-Driven Location Sharing Application, CHI 2011
  • ©2011CarnegieMellonUniversity:19 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.”
  • ©2011CarnegieMellonUniversity:20 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
  • ©2011CarnegieMellonUniversity:21 How Does Foursquare Work?
  • ©2011CarnegieMellonUniversity:22 How Does Foursquare Work? Leave tips for others
  • ©2011CarnegieMellonUniversity:23 How Does Foursquare Work? Earn badges for activities
  • ©2011CarnegieMellonUniversity:24 How Does Foursquare Work? Become mayor of a place if you have most check-ins in past 60 days Wean Hall http://foursquare.com/venue/209221 Gates http://foursquare.com/venue/174205 CIC http://foursquare.com/venue/175395
  • ©2011CarnegieMellonUniversity:25 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
  • ©2011CarnegieMellonUniversity:26 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)
  • ©2011CarnegieMellonUniversity:27 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
  • ©2011CarnegieMellonUniversity:28 Privacy Issues Why 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
  • ©2011CarnegieMellonUniversity:29 Privacy Issues
  • ©2011CarnegieMellonUniversity:30 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
  • ©2011CarnegieMellonUniversity:31 Talk Outline • System architectures – Architectures for location-based content • User studies – Why do people use foursquare? • User interfaces and policies – Large scale analysis of location traces
  • ©2011CarnegieMellonUniversity:32 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
  • ©2011CarnegieMellonUniversity:33 • Insert graph here • Describe entropy
  • ©2011CarnegieMellonUniversity:34 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 Cranshaw et al, Bridging the Gap Between Physical Location and Online Social Networks, Ubicomp 2010
  • ©2011CarnegieMellonUniversity:35 35 Using features such a location entropy significantly improves performance over shallow features such as number of co-locations
  • ©2011CarnegieMellonUniversity:36 36 Inte nsity fe a ture s Inte nsity fe a ture s Numberof co- locations Numberof co- locations W ithout intensity Full m odel
  • ©2011CarnegieMellonUniversity:37 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) Cranshaw et al, Bridging the Gap Between Physical Location and Online Social Networks, Ubicomp 2010
  • ©2011CarnegieMellonUniversity:38 Entropy Related to Location Privacy
  • ©2011CarnegieMellonUniversity:39 Ongoing Work: Understanding Human Behavior at Large Scales • What does me going to a place say about me and that place? • Scale up to thousands of people, what does it say about people in a city?
  • ©2011CarnegieMellonUniversity:40 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?
  • ©2011CarnegieMellonUniversity:41 Understanding Human Behavior at Large Scales • Get location from thousands of people in a city – Or, what if we could give smart phone to every incoming freshman? – Incentivizing people to share • Ways of sharing data while maintaining privacy of individuals? – Very high cost in collecting data – How to offer k-anonymity (or other) guarantees? – Privacy server rather than sharing data
  • ©2011CarnegieMellonUniversity:42 Acknowledgements Shah Amini Justin Cranshaw Jialiu Lin Janne Lindqvist Jason Wiese Karen Tang Eran Toch Guang Xiang Lorrie Cranor Norman Sadeh Cylab Google Intel Research Portugal
  • ©2011CarnegieMellonUniversity:43 Enhanced Social Graph • Family, friends, co-workers, acquaintances all mixed together • Family friends and high school friends • Friends and boss • My personal use
  • ©2011CarnegieMellonUniversity:44 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
  • ©2011CarnegieMellonUniversity:45 Research Angle of Attack Sensed Data Location, sound, proximity, motion Computer Data Facebook, Call Logs, SMS logs Intermediate Metrics Characterize People and Places at Large Scale Human Phenomena We Care About Privacy, Health Care, Relationships, Info Overload, Architecture, Urban Design PrivacyModels
  • ©2011CarnegieMellonUniversity:46 End-User Privacy in HCI • 137 page article surveying privacy in HCI and CSCW Iachello and Hong, End-User Privacy in Human-Computer Interaction, Foundations and Trends in Human-Computer Interaction
  • ©2011CarnegieMellonUniversity:47 WYEP Summer FestivalBlizzard …same guyTrigger happy guyRandom peak EventEvent Non-eventNon-event 2010 Photos in Pittsburgh
  • ©2011CarnegieMellonUniversity:48
  • ©2011CarnegieMellonUniversity:49 Sharing One’s Location • Place naming – “Hey mom, I am at 55.66N 12.59E.” 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
  • ©2011CarnegieMellonUniversity:50 Sharing One’s Location • Location abstractions share nothing & no social benefits share precise location (GPS) & max social benefits
  • ©2011CarnegieMellonUniversity:51 Sharing One’s Location • Location abstractions share nothing & no social benefits share precise location (GPS) & max social benefits use location abstractions to scaffold privacy concerns use location abstractions to scaffold privacy concerns
  • ©2011CarnegieMellonUniversity:52 Sharing One’s Location • Location abstractions type of description example geographic 100 Art Rooney Ave Near Golden Triangle Downtown Pittsburgh semantic Heinz Field Steelers vs. Bengals Steelers’ home Football field
  • ©2011CarnegieMellonUniversity:53 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
  • ©2011CarnegieMellonUniversity:54 Calculating Entropy from Flickr
  • ©2011CarnegieMellonUniversity:55 Foursquare Check-in Data • Viz of 566k check-ins in NYC