Privacy in Mobile Personalized Systems - The Effect of Disclosure Justifications

Privacy in Mobile Personalized SystemsThe Effect of Disclosure Justifications

Bart P. KnijnenburgDepartment of Informatics, UC Irvine

Samsung R&D Research

Alfred KobsaDepartment of Informatics, UC Irvine

Gokay SaldamliSamsung R&D Research

INFORMATION AND COMPUTER SCIENCES

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)


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 choicePrevious research: justifications


Justification types

Explain the reason why the information is requestedMay prove the legitimacy of the disclosure request

Highlight the benefits of disclosurePrivacy 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


Gender, etc.

Manipulations

Context data first Demographical data first

Location, etc.

Location, etc.

Gender, etc.


Manipulations

5 justification typesNoneUseful for youNumber of othersUseful for othersExplanation


Which one is best?

Which increases disclosure the most?

Which increases satisfaction the most?


0%#

10%#

20%#

30%#

40%#

50%#

60%#

70%#

80%#

90%#

100%#Context#first# Demographics#first# Context#first# Demograpics#first#

Disclosure*behavior**

Demographics*disclosure * *Context*disclosure*

Results


*"

1"

**"*"

***"*"

*"

0%"

10%"

20%"

30%"

40%"

50%"

60%"

70%"

80%"

90%"

100%"Context"first" Demographics"first" Context"first" Demograpics"first"



none" useful"for"you" #"of"others" useful"for"others" explanaDon"

Results


***"***"

**"

#1,00"

#0,75"

#0,50"

#0,25"

0,00"

0,25"

0,50"

0,75"

1,00"

Perceived(value(of(disclosure(help(

Results

Perceived value of disclosure help:

3 items, e.g. “The system helped me to make a tradeoff between privacy and usefulness”

Higher for all except “number of others”

*"

1"

**"*"

***"*"

*"

0%"

10%"

20%"

30%"

40%"

50%"

60%"

70%"

80%"

90%"






*"

#1,00"

#0,75"

#0,50"

#0,25"

0,00"

0,25"

0,50"

0,75"

1,00"

Perceived(privacy(threat(

Results

Perceived privacy threat:3 items, e.g. “The system has too much information about me”

Higher for “useful for others”

*"

1"

**"*"

***"*"

*"

0%"

10%"

20%"

30%"

40%"

50%"

60%"

70%"

80%"

90%"






**"1"

$1,00"

$0,75"

$0,50"

$0,25"

0,00"

0,25"

0,50"

0,75"

1,00"

Trust&in&the&&company&

Results

Trust in the company:4 items, e.g. “I believe this company is honest when it comes to using the information I provide”

Generally lower, especially for “useful for others”

*"

1"

**"*"

***"*"

*"

0%"

10%"

20%"

30%"

40%"

50%"

60%"

70%"

80%"

90%"






**" **"***"

1"

$1,00"

$0,75"

$0,50"

$0,25"

0,00"

0,25"

0,50"

0,75"

1,00"

Sa#sfac#on)with))the)system)

Results

Satisfaction with the system:6 items, e.g. “Overall, I’m satisfied with the system”

Lower for any justification!

*"

1"

**"*"

***"*"

*"

0%"

10%"

20%"

30%"

40%"

50%"

60%"

70%"

80%"

90%"






Conclusion

Justifications did not have the expected effectsNo increase in disclosureNo 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 persuasionBut participants liked the disclosure help

Possible reason 2: Low percentages cause disappointmentDisclosure only starts to increase at around 90% for the “number of others” justification

Possible reason 3: Justifications carry an implicit warningThey signal that the disclosure decision is not trivial


Discussion

None of our justification messages seemed to work very wellIs there a “golden justification”?

Different justifications may work for different types of usersHas anyone tried “tailored” disclosure help?

We provided objective information for privacy decisionsShould we do this even if it reduces users’ satisfaction?

Thank [email protected] :: www.usabart.nl :: @usabart

mailto:[email protected]

mailto:[email protected]

http://www.usabart.nl

http://www.usabart.nl


Discussion

None of our justification messages seemed to work very wellIs there a “golden justification”?

Different justifications may work for different types of usersHas anyone tried “tailored” disclosure help?

We provided objective information for privacy decisionsShould we do this even if it reduces users’ satisfaction?

Privacy in Mobile Personalized Systems - The Effect of Disclosure Justifications

Education

Transcript of Privacy in Mobile Personalized Systems - The Effect of Disclosure Justifications