see it, shake it, set it
TRANSCRIPT
SEE IT, SHAKE IT, SET ITprivacy awareness and control for mobile applications
Arosha K. BandaraThe Open University, UK
Mobile East ConferenceJune 2012
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
RESEARCH TEAM
• Bashar Nuseibeh• Yvonne Rogers• Clara Mancini• Arosha K. Bandara• Blaine Price• Lukasz Jedrejcyzk• Keerthi Thomas
• Adam Joinson
•Morris Sloman• Alessandra Russo• Emil Lupu•Naranker Dulay•Domenico Corapi• Ryan Wishart
PRIVACY THEORY
• Bi-directionality (Altmann)
• Output: sharing information with others
• Input: sensing activity of others, previous experience, etc.
StatusUpdate
Photographs
Location
12
PRIVACY THEORY
• Social translucence (Erickson and Kellog)
• Visibility
• Awareness
• Accountability
• Enforces social norms.
RESEARCH CHALLENGES
• Understand people, their behaviour and requirements.
RESEARCH CHALLENGES
• Understand people, their behaviour and requirements.
• Translate this understanding into solutions.
RESEARCH CHALLENGES
• Understand people, their behaviour and requirements.
• Translate this understanding into solutions.
• Evaluate solutions ‘in the wild’
UNDERSTANDING PEOPLE
• Investigating mobile privacy is difficult because ...
... privacy is sensitive and depends on socio-cultural context.
... mobility introduces contextual shifts and logistical obstacles.
Centre forResearch in Computing
Mobile Facebook PracticesDr. Clara Mancini
http://primma.open.ac.uk
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UNDERSTANDING PEOPLE
• It is also difficult ...
... for people to articulate subtle concerns and preferences.
... for researchers to observe contextualised behaviour.
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.
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.
BUDDY TRACKER
Alice
Bob
1. LocationUpdates
1. Location
Updates
3. Notification2. Location Request
ContextualReal-time
LearningEngine
FEEDBACK MODES
FEEDBACK MODES
FEEDBACK MODES
SEE IT: REAL-TIME FEEDBACK
• Study 1
• Two families with mixture of relationships.
• Conducted over 3 weeks, with simple real-time feedback introduced in final week.
• Quantitative data from server logs and qualitative data from ESM and post-study interviews.
Week 158%
Week 224%
Week 318%
Location Request Frequency
SEE IT: REAL-TIME FEEDBACK
• Study 2
• 3 week study with 15 participants.
• Context-aware real-time feedback with machine learning in final week.
• Quantitative data from server logs and qualitative data from ESM and post-study interviews.
Phase 142
Phase 27
Frequency of ‘intrusive’ feedback events
SEE IT: REAL-TIME FEEDBACK
0
25
50
75
100
% A
ccuracy
U7 U8 U9 U12U14 U20 U21 U22 U23 U24 U25 U30 U31 U32 U33Participant ID
Phase 1 Phase 2
Study 2 - Feedback Accuracy
SHAKE IT: HAPTIC CONTROL
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.
PRIVACY-SHAKE
Study 3 - User evaluation
Experience is ...
Strongly Disagree
Disagree Neutral AgreeStrongly
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
PRIVACY-SHAKE
Study 3 - User evaluation
0
25
50
75
100
% Success
InitialiseIncrease Privacy
Reduce PrivacyPrivacy control task
Male Female
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.
SEE IT, SHAKE IT, SET IT
Arosha K. BandaraThe Open University, UK
[email protected] - @aroshahttp://primma.open.ac.uk
privacy awareness and control for mobile applications