Research on privacy awareness and control for mobile applications, conducted at The Open University, UK. Talk delivered at MobileEast 2012 conference.

1. SEE IT, SHAKE IT, SET IT
privacy awareness and control for mobile applications
Arosha K. Bandara
The Open University, UK
Mobile East Conference
June 2012

2. RESEARCH CONTEXT
• EPSRC Funded PRiMMA Project:
Privacy Rights Management for Mobile Applications
• Collaboration between
The Open University and Imperial College London
• Contributions include methodologies for understanding privacy
requirements, machine learning techniques, architectures for
privacy aware social networks and design of real-time feedback
mechanisms for privacy awareness and control.
http://primma.open.ac.uk

3. RESEARCH TEAM
• Bashar Nuseibeh • Morris Sloman
• Yvonne Rogers • Alessandra Russo
• Clara Mancini • Emil Lupu
• Arosha K. Bandara • Naranker Dulay
• Blaine Price • Domenico Corapi
• Lukasz Jedrejcyzk • Ryan Wishart
• Keerthi Thomas
• Adam Joinson

4. PRIVACY THEORY
• Bi-directionality (Altmann) Status
Update
• Output: sharing information Location
with others
• Input: sensing activity of
others, previous experience,
etc. 2
1
Photographs

5. PRIVACY THEORY
• Social translucence
(Erickson and Kellog)
• Visibility
• Awareness
• Accountability
• Enforces social norms.

6. RESEARCH CHALLENGES
• Understand people, their
behaviour and requirements.

7. RESEARCH CHALLENGES
• Understand people, their
behaviour and requirements.
• Translate this understanding
into solutions.

8. RESEARCH CHALLENGES
• Understand people, their
behaviour and requirements.
• Translate this understanding
into solutions.
• Evaluate solutions ‘in the wild’

9. UNDERSTANDING PEOPLE !"#$%&'($)*+",#-'./$"01
• Investigating mobile privacy is
difficult because ...
... privacy is sensitive and
depends on socio-cultural
context.
... mobility introduces
contextual shifts and logistical
obstacles.
!"##$%&'()*%+,-$".'$%'/)($0"'%"#1)-2$%&

10. UNDERSTANDING PEOPLE
• It is also difficult ...
... for people to articulate
subtle concerns and
preferences.
... for researchers to observe
contextualised behaviour.

11. EXPERIENCE SAMPLING ++
• We address these challenges
by combining a variety of
complementary, indirect
methods:
• Experience sampling
enhanced with memory
phrase.
• Individual, in-depth
deferred contextual
interviews.

12. EXPERIENCE SAMPLING ++
• We address these challenges
by combining a variety of
complementary, indirect
methods:
• Experience sampling
enhanced with memory
phrase.
• Individual, in-depth
deferred contextual
interviews.

13. BUDDY TRACKER
1. Location Contextual
Updates
Real-time
Learning
Alice Engine
3. Notification
2. Location Request
1.
Location
Updates
Bob

14. FEEDBACK MODES

15. FEEDBACK MODES

16. FEEDBACK MODES

17. SEE IT: REAL-TIME FEEDBACK
• Study 1
• Two families with mixture of Week 1 Week 2
relationships. 58% 24%
• Conducted over 3 weeks, with
simple real-time feedback Week 3
introduced in final week. 18%
• Quantitative data from server
logs and qualitative data from
ESM and post-study Location Request
interviews. Frequency

18. SEE IT: REAL-TIME FEEDBACK
• Study 2
Phase 2
• 3 week study with 15 7
participants.
• Context-aware real-time Phase 1
feedback with machine 42
learning in final week.
• Quantitative data from server
logs and qualitative data from
ESM and post-study Frequency of
interviews. ‘intrusive’ feedback events

19. SEE IT: REAL-TIME FEEDBACK
Study 2 - Feedback Accuracy
Phase 1 Phase 2
100
75
% Accuracy
50
U7 U8
U9 U12 25
U14 U20
U21 U22
U23 U24
U25 U30 0
Participant ID U31 U32
U33

20. SHAKE IT: HAPTIC CONTROL

21. PRIVACY-SHAKE
1. Initialise - vertical shake
2. Phone indicates ‘ready’
3. Set privacy - horizontal movement
- Away → Relaxed privacy settings
- Closer → Strict privacy settings
4. Privacy settings updated.

22. PRIVACY-SHAKE
Study 3 - User evaluation
Experience Strongly Strongly
Disagree Neutral Agree
is ... Disagree Agree
Enjoyable 0 2 2 7 5
Engaging 0 1 4 6 5
Pleasurable 0 2 5 5 4
Exciting 1 1 6 3 5
Fun 0 2 1 5 8
Boring 9 3 2 0 2
Frustrating 2 3 5 6 0
Annoying 2 5 5 3 1

23. PRIVACY-SHAKE
Study 3 - User evaluation
Male Female
100
75
% Success
50
25
Initialise 0
Increase Privacy
Reduce Privacy
Privacy control task

24. SEE IT, SHAKE IT, SET IT
• Context-aware real-time feedback supports bi-directionality
and social translucence in location sharing applications.
• Machine learning techniques make awareness less intrusive,
leading to greater acceptance of technology.
• Intuitive control mechanisms can be used for privacy control
actions.
• Further work is required to investigate alternative privacy
control interactions - e.g., multi-touch gestures.

25. SEE IT, SHAKE IT, SET IT
privacy awareness and control for mobile applications
Arosha K. Bandara
The Open University, UK
a.k.bandara@open.ac.uk - @arosha
http://primma.open.ac.uk