1. Four Two Rants on Mobile Computing Jason I. Hong Feb 20 2007 Carnegie Mellon University Intel Ultra-Mobile Devices Workshop

2. Two Rants on Mobile Computing Text input is terrible Facing new privacy and security risks Cross-platform issues stifle wide-scale deployment Conducting realistic user evaluations difficult

3. Rant #1 – Text Input is Terrible Standard phones –Multi-tap, 8-20 wpm, world record 29 wpm –T9, ~20 wpm Special hardware –Twiddler, ~26-47 wpm (training) Pen –QWERTY, ~34 wpm –IBM SHARK (pen), 60-70 wpm Stuck with ~20 wpm for near future

4. Rant #1 – Text Input is Terrible Observation: don’t have to support generic text input –Support input for tasks that are common when mobile inTouch –Leverage daily rhythms and real-time context –Improve group awareness and messaging GurunGo –Use existing desktop web browsing activities –Improve information retrieval while on the go

5. inTouch: Mobile Group Coordination Goal: Better coordination for small mobile groups –Contextual awareness –Contextual messaging

6. Project: InTouch It’s 4:30pm and Mom is stuck in traffic inTouch checks her calendar and sees she’s supposed to pick up Cindy from ballet

7. Project: InTouch Mom’s phone senses that she is in a traffic jam, and automatically prepares a status message Mom hits “send”, and Cindy sees that Mom is running late. Cindy decides to wait inside.

8. inTouch: Mobile Group Coordination Using context to: –Select a message template –Fill in the blanks (like a MadLib) When is contextual messaging useful? –Calendar alarms (“running late, will be there in ”) –Current activity (“I’m in a meeting, done at ”) –Daily rhythms (“Picked up kid ok” at 3PM) –Messages received (“Where r u?” -> “I am at ”) Currently developing a working prototype

9. GurunGo Goal: Make it easy to access useful information while mobile Observation #1: People still tend to print out online maps, despite having mobile device. Why? –Found it via desktop, easier to print than to copy to mobile –Slow or expensive wireless connections –Inconvenient form factor on mobile device Observation #2: People don’t do the same kind of web browsing on mobile phones as on desktops –Don’t have to support all information finding tasks, just ones more likely to be done when mobile

10. GurunGo Scenarios Idea: Tie mobile more closely with desktop You find an interesting product while browsing –Use GurunGo to copy-and-paste to mobile –Augments with product reviews –Copies to mobile –Kept until explicitly deleted As you browse web on desktop: –GurunGo scans HTML for maps –Generates speech-based directions –Copies to mobile –Directions eventually discarded after given time

11. GurunGo Usage Acquire –Let people explicitly copy-and-paste info to mobile –Let people implicitly copy info via regular web browsing GurunGo scans pages seen for potentially useful stuff Augment –Look for known data types, make mobile data more useful –Ex. Augment maps with speech-based directions Copy (to mobile in the background) Browse –Organize data based on common data types –Street addresses, product comparisons, phone #s

12. GurunGo: Speech-based Directions

13. Nice Features of GurunGo Reduces number of clicks to get to useful information –Can support specific information finding tasks while mobile –Currently: Directions, products –Future: Movies, phone #s, dates and times, recent emails Works even if you don’t have wide-area wireless –Works disconnected (no network or don’t want to pay) –Only needs personal area network (Bluetooth)

14. Rant #2: New Privacy and Security Risks Mobile devices becoming intimate part of our lives –Mobile communication –Mobile e-commerce –Sharing location information with others –Unlock doors in home Leads to lots of new risks –Mobile spyware (tracks location, already starting) –Steal and punch thru corporate firewalls –Device lost, embarrassment

15. User Controllable Privacy and Security Goal: Make it easy for people to manage privacy and security policies for pervasive computing –Simple UIs for specifying policies –Clear notifications and explanations of what happened –Better visualizations to summarize results –Machine learning for learning preferences –Start with small evaluations, continue with large-scale ones Large multi-disciplinary team and project –Six faculty, 1.5 postdocs, six students –Supported by NSF, CMU CyLab –Roughly 1 year into project

16. Contextual Instant Messaging Facilitate coordination and communication by letting people request contextual information via IM –Interruptibility (via SUBTLE toolkit) –Location (via Place Lab WiFi positioning) –Active window Developed a custom client and robot on top of AIM –Client (Trillian plugin) captures and sends context to robot –People can query imbuddy411 robot for info “howbusyis username” –Robot also contains privacy rules governing disclosure

17. Contextual Instant Messaging Privacy Mechanisms Web-based specification of privacy preferences –Users can create groups and put screennames into groups –Users can specify what each group can see

18. Contextual Instant Messaging Privacy Mechanisms Notifications of requests

19. Contextual Instant Messaging Privacy Mechanisms Social translucency

20. Contextual Instant Messaging Privacy Mechanisms Audit logs

21. People Finder Location useful for micro-coordination –Meeting up –Okayness checking Developed phone-based client –GSM localization (Intel) Conducted studies to see how people specify rules (& how well) See how well machine learning can learn preferences

22. Grey – Access Control to Resources Distributed smartphone-based access control system –physical resources like office doors, computers, and coke machines –electronic ones like computer accounts and electronic files –currently only physical doors Proofs assembled from credentials –No central access control list –End-users can create flexible policies

23. Some Early Lessons People don’t seem to think about things in terms of privacy and security, more of value proposition Need large network effects to study some things –Right now, only seeing small interesting results –Believe we will find interesting results with LOTS of people Machine learning seems promising Social psychology issues –Projecting a desired persona, plausible deniability Cornwell, J., et al. User-Controllable Security and Privacy for Pervasive Computing. In the Proceedings of The 8th IEEE Workshop on Mobile Computing Systems and Applications (HotMobile 2007).

24. Other Rants (Briefly) Rant #3 – Cross-platform issues stifling wide-scale deployability –Symbian, Nokia, Palm, Windows Mobile, Blackberry –All incompatible! –J2ME only helps a little –Severely limits deployability and usage of apps Rant #4 – Conducting realistic user evals difficult –Hard to do lab studies since (by definition) mobile –Hard to observe while mobile –Majority of people already have phones (contacts, phone#)

25. Summary Text input is terrible –Likely we will be stuck with 20wpm –Leverage real-time context to support specific mobile information finding tasks rather than generic ones Facing new privacy and security risks –This may be an Achilles’ heel for pervasive computing Hard, and lots of devices to manage –Our work looks at making it easy for people to specify, visualize, and manage their privacy and security policies

26. Backup Slides

27. Usability Issues ~20% of WiFi access points returned –People couldn’t figure out how to make it work My guess: ~80% of unsecured WiFi access points –When you are mobile, risk of eavesdroppers –Computer security too hard to understand, too hard to setup

28. Usability Issues Phishing really really works –Exact numbers hard to find, but LOTS of people fall for them Semantic gap between us and everyday users –SSL, certificates, encryption, man-in-the-middle attacks –But simple phishing is stunningly effective Observation: need security models that are invisible (managed by others) or extremely easy to understand “Civilization advances by extending the number of operations we can perform without thinking about them.” - Alfred North Whitehead

29. Cultural Issues Browser Cookies –Originally meant for maintaining state –Now a pervasive means for tracking people online –Embedded in every browser, hard to change Observation: Security hard issue to wrap brain around –Hard to assess risk of low-probability event in future –Adds to cost of development for uncertain benefit –Thus, often done as an afterthought (ie too late)

30. Economic Issues Estimated cost of phishing in US is ~$5 billion Solutions already exist –Two-factor authentication –Email authentication But: –Non-computer scams ~$200 billion –Estimated cost of implementation > $5 billion Observation: Many solutions are out there, but: –Need to align needs of various parties (politics) –Need incentives (cost-benefit, law) Observation: Scammers getting more sophisticated –Market for scammers (setup + steal, mules, bookkeeping) –“Build it, and scammers will also come”

31. No Secure Mobile Computing Soon Lots of important info on mobile devices Usability issues Cultural issues Economic issues IEEE Computer, Dec 2005 “Minimizing Security Risks in Ubicomp Systems” Invisible Computing Column

32. GurunGo: Product Reviews

33. Rant #2: New Privacy and Security Risks This was just March 2006