©2009 Carnegie Mellon University : 1 An Overview of Location Privacy for Mobile Computing Jason Hong

©2011 Carnegie Mellon University : 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 2 [Berg Insight ‘10]

©2011 Carnegie Mellon University : 3 Location-Based Services Growing 3

©2011 Carnegie Mellon University : 4 Lots of Location-Based Services 4 Claims over 5 million users

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 9 How You Get and Use Location Some location-based content, even if old, still useful Different time-to-live Shah 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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?

©2011 Carnegie Mellon University : 12 Feasibility of Pre-Fetching 5mi Work Home

©2011 Carnegie Mellon University : 13 Feasibility of Pre-Fetching Radius 5mi 10mi 15mi Locaccino 86% 87% Place Naming 79% 84% 86%

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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.”

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 20 How Does Foursquare Work?

©2011 Carnegie Mellon University : 21 How Does Foursquare Work? Leave tips for others

©2011 Carnegie Mellon University : 22 How Does Foursquare Work? Earn badges for activities

©2011 Carnegie Mellon University : 23 How Does Foursquare Work? Become mayor of a place if you have most check-ins in past 60 days Wean Hall Gates

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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)

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 27 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

©2011 Carnegie Mellon University : 28 Privacy Issues

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 31 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

©2011 Carnegie Mellon University : 32 Sharing One’s Location Location abstractions share nothing & no social benefits share nothing & no social benefits share precise location (GPS) & max social benefits share precise location (GPS) & max social benefits

©2011 Carnegie Mellon University : 33 Sharing One’s Location Location abstractions share nothing & no social benefits share nothing & no social benefits share precise location (GPS) & max social benefits share precise location (GPS) & max social benefits use location abstractions to scaffold privacy concerns

©2011 Carnegie Mellon University : 34 Sharing One’s Location Location abstractions type of descriptionexample geographic 100 Art Rooney Ave Near Golden Triangle Downtown Pittsburgh semantic Heinz Field Steelers vs. Bengals Steelers’ home Football field

©2011 Carnegie Mellon University : 35 Sharing One’s Location Place entropy

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 37 Insert graph here Describe entropy

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : Using features such a location entropy significantly improves performance over shallow features such as number of co-locations

©2011 Carnegie Mellon University : Intensity features Number of co- locations Without intensity Full model

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 42 Entropy Related to Location Privacy

©2011 Carnegie Mellon University : 43 Ongoing Work Managing geotagged photos Enhanced social graph Understanding real-world human behavior at large scales

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 45 Calculating Entropy from Flickr

©2011 Carnegie Mellon University : 46 Foursquare Check-in Data Viz of 566k check-ins in NYC

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 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

©2011 Carnegie Mellon University : 49 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?

©2011 Carnegie Mellon University : 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?

©2011 Carnegie Mellon University : 51 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? New metrics to describe people and places – Churn, transience, burst Ways of sharing data with other researchers 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

©2011 Carnegie Mellon University : 52 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 Privacy Models

©2011 Carnegie Mellon University : 53 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

©2011 Carnegie Mellon University : 54

©2011 Carnegie Mellon University : 55 WYEP Summer FestivalBlizzard…same guyTrigger happy guyRandom peak Event Non-event 2010 Photos in Pittsburgh

©2011 Carnegie Mellon University : 56