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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

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

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


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


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


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


Experiment


Manipulations

Location, etc. Gender, etc.

Gender, etc. Location, etc.

Context data ﬁrst Demographical data ﬁrst


Manipulations

5 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*
*

Demographics*disclosure * *Context*disclosure*
Context#first# Demographics#first# Context#first# Demograpics#first#
100%#
90%#
80%#
70%#
60%#
50%#
40%#
30%#
20%#
10%#
0%#


Results
Disclosure*behavior*
*

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"


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%"


Results
Perceived(privacy(
Disclosure*behavior* threat(
*

Perceived privacy threat:
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%"


Results
Trust&in&the&&
Trust in the company: Disclosure*behavior* company&
*

4 items, e.g. “I believe this
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%"


Results
Sa#sfac#on)with))
Disclosure*behavior* the)system)
*

Satisfaction with the system:
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%"


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!


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


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?


Thank you
bart.k@uci.edu :: www.usabart.nl :: @usabart

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Privacy in Mobile Personalized Systems - The Effect of Disclosure Justifications

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