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Computer Human Interaction:Mobility, Privacy, and Security

Jason Hongjasonh@cs.cmu.edu

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Two Major Research Thrusts

• Streamlining Authentication– How to simplify and strengthen

authentication using sensor data?

• Understanding Human Behavior at Large Scales– What can we infer about people and

places based on lots of sensor data?

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Too many passwords!!!

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Problems with Passwords

• People forget passwords• Susceptible to social engineering• People re-use passwords• Passwords tend to be weak in practice

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WebTicket

• Cheap printable tokensfor a reliable way to log in

• Browser plug-in forcreating new accounts– Strong passwords are assigned

• Print out ticket– Ticket is encrypted to work

only with specific computer(s)– QRCode: URL, user name,

password

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Logging In with WebTicket

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WebTicket

• Design:– Very cheap (paper + printer + webcam)– Compatible with existing systems– Easy to deploy– Easy to teach: treat it like a house key

• Weaknesses:– Not meant for commonly used passwords– Tickets can get damaged or lost– Need to store main encryption key– Scale (about 15 accounts on average)

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WebTicket User Study

• Three studies, 59 people total– Study 1: Lab study– Study 2: Lab study (phishing too)– Study 3: Field trial

• Experiment– Two conditions: password and WebTicket – Create a few new accounts– Login to a few sites– Come back a week later, login again

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WebTicket Study Results

• 1/4 of people using passwords could not login again a week later– Didn’t restrict what passwords people used

• Login time for WebTicket slower at first, faster a week later

• WebTicket perceived as easier and faster• Simulated phishing attack– All in password condition fell for it– 30% of people using WebTicket did

(though data still encrypted)

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Ongoing and Future Work

• Mobile phone version to scale up– A strong password manager– Can’t fall for phish too

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Ongoing Work

• Can encode 3k data with QR codes– Ex. “Login only if in Cylab office or home”– Ex. “Login only if between 5-8pm”– Ex. “Login only if parents at home”– Ex. “Notify parents when you login”– Ex. Include face biometric data

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Casual Authentication

• Use commodity sensors + behavioral models for cheap, passive, multi-factor authentication

• Modulate level of authentication needed– In likely situations, make logins fast– In unlikely situations, make it reliable

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Example Scenarios

• Scenario 1 – Mobile device– If in office is high, make login fast– If in Brazil, make login reliable– Location, IP address, WiFi MAC,

Bluetooth devices nearby, tilt

• Scenario 2 – Home– Wake up in morning, go to computer– Weight sensor in chair, height sensor

via Kinect, mobile devices nearby– Use face recognition to login (fast)

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Casual Authentication

• Location as a passive factor– (a) Diary study with 20 people– (b) Location traces of 30 people

(a) Where people login

(Hayashi and Hong, CHI 2011)

(b) Where peoplespend time

(Amini et al, Mobisys 2011)

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• Location entropy– Concept taken from ecology– Number of unique people seen in a place– Approximates public vs private

• Locaccino data– 489 participants– 2.8m location sightings

Characterizing Places

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Using Location Data

• Characterizing individuals– Personal frequency– Personal mobility pattern

• Characterizing places– Entropy – number of unique people– Churn – same people or different– Transience – amount of time spent– Burst – regularity of people seen

• Building models of people and places

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Ongoing Work

• Evaluating passive factors• Developing threat models– How well person knows you– How skilled a hacker they are

• Developing prototypes– Mobile case– Work/Home

• Evaluating security and usability– Ease of use, time to login– False accept rates, expert analysis

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Understanding Human Behavior at Very 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

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• Insert graph here• Describe entropy

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Entropy Related to Location Privacy

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Results of Location Analysis

• Entropy related to location privacy– Fewer concerns in “public” places

(Toch et al, Ubicomp 2010)

• Can predict Facebook friendships based on co-location patterns– Not just frequency, but also where– 92% accuracy

(Cranshaw et al, Ubicomp 2010)

• Can predict number of friends based on mobility patterns– Go out often and to high entropy places

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Augmented Social Graph

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Augmented Social Graph

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Augmented Social Graph

• Online social network information + smartphone communication– Infer tie strength, roles, groups

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Potential Scenarios

• Secure invitations– Who is this person friending me?– How do my friends know her?

• Communication triage• Configuration of privacy policies– Tie strength strongly correlated with what

personal info people willing to share(Wiese et al, Ubicomp 2011)

– Communication and co-location can be used to predict tie strength

• Depression / Leadership

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Summary

• WebTicket– Printable tokens to login

• Casual authentication– Use sensor data and models to

characterize people and places– Modulate level of authentication

based on situation

• Understanding behavior at large scales– Opportunity to instrument the world– Augmented social graph