location privacy for mobile computing, cylab talk on feb 2011

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Location Privacy for Mobile Computing

Jason [email protected]

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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)

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[Berg Insight ‘10]

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Location-Based Services Growing

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Lots of Location-Based Services

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Claims over 5 million users

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5Potential 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

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6Potential 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

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7Our 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

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8Talk 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

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Location-based Content

• Some location-based content,even if old, still useful

• Different time-to-live

Amini et al, Caché: Caching Location-Enhanced Contentto 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

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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

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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?

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5mi

Work

Home

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Radius

5mi

10mi

15mi

Locaccino

86%

87%

87%

Place Naming

79%

84%

86%

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• 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

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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

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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

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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

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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

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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.”

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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

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Leave tips for others

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Earn badges for activities

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How Does Foursquare Work?Become mayor of a place if you

have most check-ins in past 60 daysWean Hall http://foursquare.com/venue/209221

Gates http://foursquare.com/venue/174205CIC http://foursquare.com/venue/175395

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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

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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)

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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

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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

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Privacy Issues

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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

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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

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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

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• Insert graph here• Describe entropy

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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

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

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Inte

nsity

feat

ures

Inte

nsity

feat

ures

Nu

mb

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of

co-l

ocati

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of

co-l

ocati

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With

out inte

nsity

Full model

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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

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Entropy Related to Location Privacy

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Ongoing Work: 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?

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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?

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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

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42Acknowledgements

Shah AminiJustin CranshawJialiu LinJanne LindqvistJason WieseKaren TangEran TochGuang Xiang

Lorrie CranorNorman Sadeh

CylabGoogleIntel ResearchPortugal

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Enhanced Social Graph

• Family, friends, co-workers, acquaintances all mixed together

• Family friends and high school friends

• Friends and boss• My personal use

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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

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Research Angle of Attack

Sensed DataLocation, sound, proximity, motion

Computer DataFacebook, Call Logs,

SMS logs

Intermediate MetricsCharacterize People and Places at Large Scale

Human Phenomena We Care AboutPrivacy, Health Care, Relationships,

Info Overload, Architecture, Urban Design

Privacy M

od

els

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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

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WYEP Summer FestivalBlizzard …same guyTrigger happy guyRandom peak

EventEvent

Non-eventNon-event

2010 Photos in Pittsburgh

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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 Preferencesin Location Sharing, Ubicomp 2010

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• Location abstractions

share nothing &

no social benefits

share precise location (GPS) &

max social benefits

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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

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type of description example

geographic 100 Art Rooney AveNear Golden TriangleDowntownPittsburgh

semantic Heinz FieldSteelers vs. BengalsSteelers’ homeFootball field

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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

location privacy for mobile computing, cylab talk on feb 2011

Technology

ubiquity of location

lots of location

location user studies

caching locationbased

locationbased services

locationenabled gps

socialdriven location

fetches content