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

1. Privacy in Mobile Personalized Systems
The Effect of Disclosure Justifications
Bart P. Knijnenburg Alfred Kobsa Gokay Saldamli
Department of Informatics, UC Irvine Department of Informatics, UC Irvine Samsung R&D Research
Samsung R&D Research

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. 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. 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. Justification types
Explain the reason why the information is requested
May prove the legitimacy of the disclosure request
Highlight the benefits of disclosure
Privacy calculus: tip the scales in favor of the benefits
Appeal to the social norm
Eschew privacy calculus by conforming to the majority
INFORMATION AND COMPUTER SCIENCES

6. Our starting point
Previous work: Justifications seem to work
- They increase disclosure
- They increase user satisfaction
-not always tested
Our goal: Find out which one works best
INFORMATION AND COMPUTER SCIENCES

7. Experiment
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8. Experiment
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9. Manipulations
Location, etc. Gender, etc.
Gender, etc. Location, etc.
Context data first Demographical data first
INFORMATION AND COMPUTER SCIENCES

10. Manipulations
5 justification types
None
Useful for you
Number of others
Useful for others
Explanation
INFORMATION AND COMPUTER SCIENCES

11. Which one is best?
Which increases disclosure the most?
Which increases satisfaction the most?
INFORMATION AND COMPUTER SCIENCES

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. 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. 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. 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. 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. 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. Conclusion
Justifications 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. Reflection
Why did this happen?
Possible reason 1: Justifications are seen as persuasion
But participants liked the disclosure help
Possible reason 2: Low percentages cause disappointment
Disclosure only starts to increase at around 90% for the
“number of others” justification
Possible reason 3: Justifications carry an implicit warning
They signal that the disclosure decision is not trivial
INFORMATION AND COMPUTER SCIENCES

20. Discussion
None 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. Thank you
bart.k@uci.edu :: www.usabart.nl :: @usabart

22. Discussion
None 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