Slides

Report
Suman Nath
Microsoft Research
Contextual Computing
Make computing context-aware
• Context: location, activity, preference, history
• A lot of progresses in location-aware services
Not enough …
• Need to use other signals
o
o
o
o
o
Do I like Italian restaurant?
Am I walking? Do I drive 10 miles to eat?
Is it lunch time or dinner time?
Alone with family?
Queue time ?
Personal
preference/history
User’s context
Real-time status
• How do we get them?
o Ask users to release more contextual information
o Rely on crowdsourcing
• Challenges to address:
o Energy: partially solved
o Privacy: mostly unsolved
Energy
• Many services require continuous sensing
• Acquiring context is expensive
• Many optimizations proposed
o Not sufficient for continuous sensing
o Phone will die in a few hours
• Challenge: continuous sensing for a day without
charging
• Needs innovation: Efficient “Assisted” GPS
Low Power Assisted GPS
• Not regular GPS
replacement
1ms
NMS
Takes 1s to minutes
Same for ~150KM
code phase
Requires a few ms
• Mobile phone sends to server:
•
•
•
Code phases
Cell tower ID
Time stamp
• Server:
•
•
Computes NMS
Computes mobile location
• Location-based services
(e.g. mobile search)
• Batched location
estimation (e.g. path
prediction)
• Delay-tolerance
positioning (e.g. geotagging photos)
• Crowdsourcing
LEAP: A Low Energy Assisted GPS for Trajectory-Based Services, Ramos et al. Ubicomp 2011
Privacy: do we care?
• News: iPhone keeps record of everywhere you go
Do people care?
48%
52% said they were "very or
extremely concerned" about loss of
privacy from using location-sharing
applications
Are you worried about geolocation privacy?
48% seriously concerned, 32% little worried
Why is the stake high?
Apple fined 1M won ($932) by South Korea over iPhone tracking
allegations
The suit now counts 26,691 plaintiffs => $26 million
Lawmakers Demand Apple Clarify iPhone Tracking Capability
Facebook fights new California privacy bill
'Do Not Track Me Online' privacy bill introduced by California Rep.
Jackie Speier
PER Theorem
Revenue/
Relevance
Privacy
Efficiency
Impossible to maximize all three
Trivial to maximize any two
Michaela Goetz and Suman Nath, Privacy-Aware Personalization for Mobile Advertising, no. MSR-TR-2011-92, August 2011
My wishlist
• My context-aware service knows what is relevant
• Without affecting my phone battery much
• Without me telling it much about my private
context
• Even if I release limited private information
o My privacy is preserved (even with strong adversaries)
o In future I can revoke my data
o (Only) I can decide how my data is used and shared

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