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Mobile Usage Patterns and Privacy Implications

This study examines user behavior and attitudes towards privacy in mobile computing. Findings show that users exercise little caution in preserving mobile privacy, underestimate privacy threats, and have a different understanding of privacy compared to the security community. The study also explores how demographics and device ownership influence behavior and usage of privacy-enhancing tools.

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Mobile Usage Patterns and Privacy Implications

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  1. Mobile Usage Patterns and Privacy ImplicationsMichael MitchellMarch 27, 2015 RatneshPatidar, ManikSaini, Parteek Singh, An-I Wang Florida State University • Peter Reiher • University of California, Los Angeles

  2. Introduction • Privacy is a major concern for pervasive & mobile computing • Current understanding incomplete • Subjective nature of privacy • Automatic detection limited • Important to understand what privacy actually means to people • Make the right tools to fix the right problems

  3. Overview • Empirical data on user privacy behavior limited • Conducted a survey-based study of ~600 users • Major findings include: • (1) People exercise little caution preserving mobile privacy • (2) Privacy is not equal to trust • (3) Users underestimate mobile app privacy threats • (4) Users’ understanding of privacy is different from that of the security community

  4. Research Questions • Primary survey goal: examine how mobile users feel about privacy • What does it mean to be private? • Do users alter computing behavior in certain environments? Around certain people? • Secondary goal: understand user behavior and general mobility patterns • Where, when, and how mobile devices are used • Does gender, ethnicity, age, income, choices of technology, or technical sophistication influence behavior?

  5. Background & Early Challenges • Privacy subjective, requires human interaction • Human Subject (IRB) Approval • Participant recruitment • Participant motivation and compensation

  6. Mobile Usage Questionnaire • ~100 questions in total via mobile app & web • Questions cover: • Background, demographics, hardware ownership • Computing tasks performed by location in public and private • Where/when/why behavior changes • Usage of privacy/security tools • $1000 was allocated for prizes • Chance to win one of 66 $15 Starbucks gift cards

  7. Participant Demographics • FSU Survey • 292 total participants • Median age of 22; 6 years computing experience • Craigslist Survey • 303 total participants • Median age of 27; 6 years computing experience • Few differences observed between surveys • Unless otherwise noted all results reflective of combined 595 responses

  8. Participant Demographics

  9. Device Market Share • Phones & tablets of survey participants reflect U.S. market share • Within 7% of target demographics • Slightly more Apple, slightly fewer Android • Not quite as reflective of laptops • Fewer Windows users (by 28%) • More Apple (by 21%) and Linux users (by 7%)

  10. Device Ownership • Does hardware preference play a role in mobility or privacy? • Relationship between brands and behaviors? • Men, tech-savvy users, and minorities • Own Android devices (up to 20%) • Own Windows laptops (up to 19%)

  11. Brand Homogeneity • Participant brand loyalty • iPhone owners more frequently own an Apple laptop or tablet (by up to 28%) • Android owners more frequently own an Android tablet (by 15%) • More pronounced in FSU data set • iPhone owners more frequently own Apple laptops and tablets (by up to 40%)

  12. Computing Locations

  13. Most Common Public & Private Tasks Top 5 tasks significantly more frequent Most have little difference in public/private

  14. Categorical Public & Private Tasks Top 2 categories significantly more frequent Most have little difference in public/private

  15. Public & Private Tasks by Risk Level More often in private Little difference in public/private?

  16. Public & Private Activity Overall • Behavioral differences in public and private among groups not statistically significant • Genders, technical backgrounds, and ethnicities • A few exceptions: • Women use social networking more frequently than men in public and private (up to 40%) • Tech-savvy users more likely to email in public and private (up to 24%),

  17. Who Makes Users Change Behavior? More familiar > 10% Never change behavior Less familiar

  18. Usage of Privacy Enhancing Tools Technical background more likely to encrypt Differences less pronounced for password vaults

  19. OS & App Permission Compliance More likely to comply with apps than OS?

  20. Implications of Apple Ownership? • Compared to Android owners, Apple users: • Use devices more in public locations (up to 16%) • Use their devices more for most social mobile computing tasks • Texting, e-mailing, and social networking (up to 63%) • Have less regard for security • WiFi - 86% of iPhone owners use open, public networks without security, (6% above average) • Less likely to use encryption (by 7%)

  21. Survey Lessons • Survey speaks to user attitudes towards privacy, not necessarily actual behavior • User attitudes critical in determining success of a privacy or security measure • As important to a privacy mechanism’s success as the technical details of how it works? • Important for developers of mobile and pervasive privacy preserving mechanisms

  22. Privacy Implications on Systems • Users are far more concerned about protecting their privacy from familiar people • Parents twice the privacy threat as strangers? • Perhaps privacy preserving mechanisms designed to protect against family and friends? • Researchers must ensure that their goals align with users’ real privacy desires

  23. Privacy, Trust, Anonymity • Results suggest that trust and privacy are largely orthogonal • Those most trusted are also the most feared • Perhaps perception of anonymity towards strangers? • False sense of security could face serious consequences

  24. On-going/Future Work • Reported behavior = actual behavior? • On-going long term usage study • 35 selected users over three months • Developed “Big Brother” Android firmware • Tracks location, usage, histories, etc. • Compare actual usage with user reported changes • Determine if users actually behave how they claim

  25. Conclusion • Users not concerned about preserving mobile privacy? • Even tech-savvy users do not alter their behavior based on their surroundings • Obvious critical question: • Users unaware of the risks? Or • Aware and simply do not care? • If users don’t care about privacy, only the least intrusive mechanisms will succeed • Philosophically, is it even our business to care?

  26. Thank you Mobile Usage Patterns and Privacy ImplicationsMichael Mitchellmitchell@cs.fsu.edu • All interaction with human subjects was approved by the Florida State University IRB Human Subjects Committee, approval number 2013.10175. • This work is sponsored by NSF CNS-1065127. • Opinions, findings, and conclusions or recommendations expressed in this document do not necessarily reflect the views of the NSF, FSU, UCLA, or the U.S. government.

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