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 Do I like Italian restaurant? o Am I walking? Do I drive 10 miles to eat? o Is it lunch time or dinner time? o Alone with family? o Queue time ? 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 Personal preference/history User’s context Real-time status

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 Location-based services (e.g. mobile search) Batched location estimation (e.g. path prediction) Delay-tolerance positioning (e.g. geo- tagging photos) Crowdsourcing  code phase NMS 1ms Requires a few ms Takes 1s to minutes Same for ~150KM Mobile phone sends to server: Code phases Cell tower ID Time stamp Server: Computes NMS Computes mobile location 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? 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 48%

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 Impossible to maximize all three Trivial to maximize any two Privacy Revenue/ Relevance Efficiency Server-side Client-side No-result Michaela Goetz and Suman Nath, Privacy-Aware Personalization for Mobile Advertising, no. MSR-TR , 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