privacygrade and social cybersecurity, talk at ftc july 2015

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©2015 Carnegie Mellon University : 1 PrivacyGrade and Social Cybersecurity Jason Hong Federal Trade Commission July 9, 2015 Computer Human Interaction: Mobility Privacy Security

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Page 1: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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PrivacyGrade andSocial Cybersecurity

Jason Hong

Federal Trade CommissionJuly 9, 2015

ComputerHumanInteraction:MobilityPrivacySecurity

Page 2: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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Talk Overview

• PrivacyGrade– Analyzing the privacy of 1M

smartphone apps

• Social Cybersecurity– Using social psych to influence

people’s cybersecurity behaviors

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What Are Your Apps Really Doing?

Shares your location,gender, unique phone ID,phone# with advertisers

Uploads your entire contact list to their server(including phone #s)

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Many Smartphone Apps Have “Unusual” Permissions

Location Data Unique device ID

Location Data Network Access

Unique device ID

Location Data Microphone

Unique device ID

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What Do Developers Know about Privacy?

• Interviews with 13 app developers• Surveys with 228 app developers

• What tools? Knowledge? Incentives?• Points of leverage?

Balebako et al, The Privacy and Security Behaviors of Smartphone App Developers. USEC 2014.

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Summary of FindingsThird-party Libraries Problematic

• Use ads and analytics to monetize

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Summary of FindingsThird-party Libraries Problematic

• Use ads and analytics to monetize• Hard to understand their behaviors

– A few didn’t know they were using libraries (inconsistent answers)

– Some didn’t know they collected data– “If either Facebook or Flurry had a

privacy policy that was short and concise and condensed into real English rather than legalese, we definitely would have read it.”

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Summary of FindingsDevs Don’t Know What to Do

• Low awareness of existing privacy guidelines– Often just ask others around them

• Low perceived value of privacy policies– Mostly protection from lawsuits– “I haven’t even read [our privacy

policy]. I mean, it’s just legal stuff that’s required, so I just put in there.”

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PrivacyGrade.org• Improve transparency• Assign privacy grades to

all 1M+ Android apps

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Page 11: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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Page 12: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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Page 13: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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Page 14: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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Expectations vs Reality

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Privacy as Expectations

Use crowdsourcing to compare what people expect an app to do vs what an app actually does

App Behavior(What an app actually does)

User Expectations(What people think

the app does)

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How PrivacyGrade Works

• Long tail distribution of libraries• We focused on top 400 libraries

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How PrivacyGrade Works

• We crowdsourced people’s expectations of core set of 837 apps– Ex. “How comfortable are you with

Drag Racing using your location for ads?”

• Created a model to predict people’s likely privacy concerns

• Applied model to 1M Android apps

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Overall Stats on PrivacyGradeApril 2015

• No sensitive permissions used means A+

• Other gradesset at quartilesof grade range

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Changes in Grades Over TimeOctober 2014 to April 2015

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Changes in Grades Over TimeMost Grades Remained the Same

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Changes in Grades Over TimeA Fair Number of Apps Improved

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Changes in Grades Over TimeLots of Apps Deleted

• Not sure why deleted yet– Some apps were re-uploaded

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Impact of this Research

• Popular Press– NYTimes, CNN, BBC, CBS, more

• Government– Earlier work helped lead to FTC fines– Scared some Congressional staffers

• Google• Developers

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Social Cybersecurity

• New work looking at changing people’s awareness, knowledge, and motivation to be secure

• Tool for FTC and companies to use to improve privacy and security

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Social Proof

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• Baseline effectiveness is 35%

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• “showing each user pictures of friends who said they had already voted, generated 340,000 additional votes nationwide”

• “they also discovered that about 4 percent of those who claimed they had voted were not telling the truth”

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Adoption of Cybersecurity Features is Very Low

• Typically single digits– Two-factor authentication– Login notifications on Facebook– Trusted contacts on Facebook

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Insight from Interviews Observability of Adoption Low

• One person stopped in coffee shop and asked about the Android 9-dot:

“We were just sitting in a coffee shop and I wanted to show somebody something and [they said], ‘My phone does not have that,’ and I was like, ‘I believe it probably does.’”

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Diffusion of Innovations

• Five major factorsfor successful innovations:– Relative Advantage– Trialability– Complexity– Compatibility– Observability

Page 32: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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Social Proof + Making Cybersecurity Observable

• Variants– Control– Over # / %– Only # / %– Raw # / %– Some

Das, S., A. Kramer, L. Dabbish, J.I. Hong. Increasing Security Sensitivity With Social Proof: A Large-Scale Experimental Confirmation. CCS 2014.

Page 33: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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Method

• Controlled, randomized study with 50k active Facebook users– 8 conditions, so N=6250

• Part of annual security awareness campaign Facebook was going to run anyway

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Results of Experiment

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Summary

• PrivacyGrade– Analyzing the privacy of 1M apps

• Social Cybersecurity– Social proof + observability to improve

cybersecurity behaviors

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Thanks!

Collaborators:

Special thanks to:• Army Research Office• National Science Foundation• Alfred P. Sloan Foundation

• Google• CMU Cylab• NQ Mobile

• Shah Amini• Kevin Ku• Jialiu Lin

• Song Luan• Bharadwaj Ramachandran• Norman Sadeh

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How PrivacyGrade Works

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Limitations of Current Approach• PrivacyGrade works for most apps

– But popular apps, lots of custom code– Also can’t analyze backend

• Only free apps– Limitations on downloading paid apps

• Assume most libraries have one purpose– True for vast majority– More analytics + advertising combos

Page 39: PrivacyGrade and Social Cybersecurity, talk at FTC July 2015

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Talk Overview

• Interviews and surveys of app developers

• PrivacyGrade.org• Using text mining to infer

privacy-related app behaviors• Reflections on privacy ecosystem

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Reflections on PrivacyConsider entire ecosystem

• End-users– Most research has focused here– But puts too much burden– Really hard to improve awareness,

knowledge, and motivation

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Reflections on PrivacyConsider entire ecosystem

• End-users• Developers• Third-party developers• Markets• OS• Third-party advocates

– Ex. FTC, Consumer Reports

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Reflections on PrivacyHelping Developers• Point of greatest leverage• Examples:

– Better understanding of 3rd party libs– Better design patterns for privacy– Better APIs

• “Home” or “work” vs precise location

– Better reusable components• Databases and ACID properties

• Make the path of least resistance privacy sensitive

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Mobile App

• Scans apps youhave on phone, gets grades from our site

• Just need to add it toGoogle Play store