Privacy in Mobile Personalized Systems                           The Effect of Disclosure Justifications      Bart P. Knijn...
Mobile apps need personal data                 Mobile applications often                 use personalization              ...
Let users control their disclosure                  Problem: Many people are                  not comfortable disclosing  ...
Help users decide what to disclose                 Problem: This trade-off is                 difficult!                   ...
Justification typesExplain the reason why the information is requested   May prove the legitimacy of the disclosure request...
Our starting pointPrevious work: Justifications seem to work - They increase disclosure - They increase user satisfaction  ...
Experiment             INFORMATION AND COMPUTER SCIENCES
Experiment             INFORMATION AND COMPUTER SCIENCES
Manipulations      Location, etc.    Gender, etc.  Gender, etc.               Location, etc.   Context data first   Demogra...
Manipulations5 justification types   None   Useful for you   Number of others   Useful for others   Explanation            ...
Which one is best?Which increases disclosure the most?Which increases satisfaction the most?                              ...
Results                              Disclosure*behavior*                                              *           Demogra...
Results                                   Disclosure*behavior*                                                   *        ...
Results                                                      Perceived(value(of(Perceived value of                        ...
Results                                                      Perceived(privacy(                                  Disclosur...
Results                                                                          Trust&in&the&&Trust in the company:      ...
Results                                                       Sa#sfac#on)with))                                  Disclosur...
ConclusionJustifications did not have the expected effects   No increase in disclosure   No decrease in perceived threat, n...
ReflectionWhy did this happen?Possible reason 1: Justifications are seen as persuasion   But participants liked the disclosu...
DiscussionNone of our justification messages seemed to work very well   Is there a “golden justification”?Different justifica...
Thank youbart.k@uci.edu :: www.usabart.nl :: @usabart
DiscussionNone of our justification messages seemed to work very well   Is there a “golden justification”?Different justifica...
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Privacy in Mobile Personalized Systems - The Effect of Disclosure Justifications

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Paper Presentation at the Workshop on Usable Privacy & Security for Mobile Devices (U-PriSM) at the Symposium On Usable Privacy and Security (SOUPS) 2012

Paper can be found here: http://appanalysis.org/u-prism/soups12_mobile-final11.pdf
Full journal paper (under review): http://bit.ly/TiiSprivacy

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Privacy in Mobile Personalized Systems - The Effect of Disclosure Justifications

  1. 1. Privacy in Mobile Personalized Systems The Effect of Disclosure Justifications Bart P. Knijnenburg Alfred Kobsa Gokay SaldamliDepartment of Informatics, UC Irvine Department of Informatics, UC Irvine Samsung R&D Research Samsung R&D Research
  2. 2. Mobile apps need personal data Mobile applications often use personalization This requires personal information - Demographical data (e.g. age, hobbies, income) - Contextual data (e.g. app usage, calendar, location) INFORMATION AND COMPUTER SCIENCES
  3. 3. Let users control their disclosure Problem: Many people are not comfortable disclosing diverse personal information FTC, CPBoR: let users decide Privacy calculus: trade off between benefits and risks INFORMATION AND COMPUTER SCIENCES
  4. 4. Help users decide what to disclose Problem: This trade-off is difficult! Lack of knowledge about positive and negative consequences CPBoR: informed choice Previous research: justifications INFORMATION AND COMPUTER SCIENCES
  5. 5. Justification typesExplain the reason why the information is requested May prove the legitimacy of the disclosure requestHighlight the benefits of disclosure Privacy calculus: tip the scales in favor of the benefitsAppeal to the social norm Eschew privacy calculus by conforming to the majority INFORMATION AND COMPUTER SCIENCES
  6. 6. Our starting pointPrevious work: Justifications seem to work - They increase disclosure - They increase user satisfaction -not always testedOur goal: Find out which one works best INFORMATION AND COMPUTER SCIENCES
  7. 7. Experiment INFORMATION AND COMPUTER SCIENCES
  8. 8. Experiment INFORMATION AND COMPUTER SCIENCES
  9. 9. Manipulations Location, etc. Gender, etc. Gender, etc. Location, etc. Context data first Demographical data first INFORMATION AND COMPUTER SCIENCES
  10. 10. Manipulations5 justification types None Useful for you Number of others Useful for others Explanation INFORMATION AND COMPUTER SCIENCES
  11. 11. Which one is best?Which increases disclosure the most?Which increases satisfaction the most? INFORMATION AND COMPUTER SCIENCES
  12. 12. Results Disclosure*behavior* * Demographics*disclosure * *Context*disclosure* Context#first# Demographics#first# Context#first# Demograpics#first#100%# 90%# 80%# 70%# 60%# 50%# 40%# 30%# 20%# 10%# 0%# INFORMATION AND COMPUTER SCIENCES
  13. 13. Results Disclosure*behavior* * Demographics*disclosure * *Context*disclosure* Context"first" Demographics"first" Context"first" Demograpics"first"100%" 90%" 1" 80%" ***" 70%" *" **" *" 60%" *" *" 50%" 40%" 30%" 20%" 10%" 0%" none" useful"for"you" #"of"others" useful"for"others" explanaDon" INFORMATION AND COMPUTER SCIENCES
  14. 14. Results Perceived(value(of(Perceived value of Disclosure*behavior* disclosure(help(disclosure help: * Demographics*disclosure * ***" *Context*disclosure* Context"first" Demographics"first" 1,00" Context"first" ***" Demograpics"first"100%" 3 items, e.g. “The system 0,75" **" 90%" helped 1" me to make a ***" 0,50" 80%" tradeoff between privacy 70%" 0,25" *" **" *" 60%" and usefulness” *" *" 0,00" 50%" #0,25"Higher for all except 40%" 30%" #0,50"“number of others” 20%" #0,75" 10%" #1,00" 0%" none" useful"for"you" #"of"others" useful"for"others" explanaDon" INFORMATION AND COMPUTER SCIENCES
  15. 15. Results Perceived(privacy( Disclosure*behavior* threat( *Perceived privacy threat: Demographics*disclosure * *Context*disclosure* Context"first" Demographics"first" 1,00" Context"first" Demograpics"first"100%" 3 items, e.g. “The system 0,75" 90%" 1" *" 80%" has too much information ***" 0,50" 70%" about me” 0,25" *" **" *" 60%" 0,00" *" *"50%"Higher for “useful for others” #0,25" 40%" 30%" #0,50" 20%" #0,75" 10%" #1,00" 0%" none" useful"for"you" #"of"others" useful"for"others" explanaDon" INFORMATION AND COMPUTER SCIENCES
  16. 16. Results Trust&in&the&&Trust in the company: Disclosure*behavior* company& * Demographics*disclosure * *Context*disclosure* 4 items, e.g. “I believe this Context"first" Demographics"first" 1,00" Context"first" Demograpics"first"100%" company is honest when 0,75" 90%" 1" 80%" it comes ***"using the to 0,50" 70%" information I provide” 0,25" *" **" *"60%" 0,00" *" *"Generally lower, especially 50%" $0,25" 40%"for “useful for others” 30%" $0,50" 1"20%" $0,75" **"10%" $1,00" 0%" none" useful"for"you" #"of"others" useful"for"others" explanaDon" INFORMATION AND COMPUTER SCIENCES
  17. 17. Results Sa#sfac#on)with)) Disclosure*behavior* the)system) * Demographics*disclosure * *Context*disclosure*Satisfaction with the system: Context"first" Demographics"first" 1,00" Context"first" Demograpics"first"100%" 0,75" 90%" 6 items,1"e.g. “Overall, I’m ***" 0,50" 80%" satisfied with the system” 70%" 0,25" *" **" *" 60%" 0,00" *" *"Lower for any justification!50%" $0,25" 40%" 30%" $0,50" 1" 20%" $0,75" **" **" 10%" $1,00" ***" 0%" none" useful"for"you" #"of"others" useful"for"others" explanaDon" INFORMATION AND COMPUTER SCIENCES
  18. 18. ConclusionJustifications did not have the expected effects No increase in disclosure No decrease in perceived threat, no increase in trust Satisfaction is lower...but participants liked the disclosure help! INFORMATION AND COMPUTER SCIENCES
  19. 19. ReflectionWhy did this happen?Possible reason 1: Justifications are seen as persuasion But participants liked the disclosure helpPossible reason 2: Low percentages cause disappointment Disclosure only starts to increase at around 90% for the “number of others” justificationPossible reason 3: Justifications carry an implicit warning They signal that the disclosure decision is not trivial INFORMATION AND COMPUTER SCIENCES
  20. 20. DiscussionNone of our justification messages seemed to work very well Is there a “golden justification”?Different justifications may work for different types of users Has anyone tried “tailored” disclosure help?We provided objective information for privacy decisions Should we do this even if it reduces users’ satisfaction? INFORMATION AND COMPUTER SCIENCES
  21. 21. Thank youbart.k@uci.edu :: www.usabart.nl :: @usabart
  22. 22. DiscussionNone of our justification messages seemed to work very well Is there a “golden justification”?Different justifications may work for different types of users Has anyone tried “tailored” disclosure help?We provided objective information for privacy decisions Should we do this even if it reduces users’ satisfaction? INFORMATION AND COMPUTER SCIENCES

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