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Privacy for Ubiquitous Computing

Joshua Sunshine. Privacy for Ubiquitous Computing. Looking Forward. Defining Ubiquitous Computing Unique Privacy Problems Examples Exercise 1: Privacy Solution Privacy Tradeoffs Professional Solutions Exercise 2: User Study Conclusion. Ubiquitous Computing Definitions.

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Privacy for Ubiquitous Computing

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  1. Joshua Sunshine Privacy for Ubiquitous Computing

  2. Looking Forward • Defining Ubiquitous Computing • Unique Privacy Problems • Examples • Exercise 1: Privacy Solution • Privacy Tradeoffs • Professional Solutions • Exercise 2: User Study • Conclusion

  3. Ubiquitous Computing Definitions • Everywhere (duh!) • Invisible • Mobile • Interoperable • Context Aware • Personal • Multi-Agent

  4. Privacy Problems • More data collected, more data to be used inappropriately (Everywhere) • User forget they are revealing private information (Invisible) • Hard to configure data sharing (Invisible, Everywhere)

  5. Privacy Problems 2 • New class of data -- contextual information (Context Aware) • Stalkers (location) • Advertisers (location, activity) • Hard To Identify Invasions (Multi-Agent) • Hard to Recover (Multi-Agent)

  6. Example, Boss

  7. Example, Mobile Phones • Problem: Interruptions • Caller doesn’t know receiver’s context • Solution: Reveal Context • Location • Activity • Company • Conversation

  8. Example, Bus Tracking • Problem: When will the next bus arrive? • Tool: Cell phones • Solution: • Aggregate information from riders phones • Send alerts to people waiting for a bus

  9. Exercise 1: Privacy Solution • Break up into two groups • Make a list of privacy problems • Come up with a solution that avoids or minimizes these problems • 10 minutes

  10. Professional Privacy Problems, Bus Tracking • Identity violation • Identity of individual is determined • Happens when identifier is sent in a report to the server • Tracking violation • Movement of individual tracked over time • Happens when identify one report as belonging to a person who sent an earlier report

  11. Professional Solution, Bus Tracking • Hitchhiking • Anonymous data collection • Location is Computed on the Client • Only the Client Device is Trusted • Report Approval • Restriction of Reports to Specific Locations

  12. User Study, Mobile Phones • Context Types: Location, Activity, Company, Conversation • Relationship Types: Significant other, family member, friend, colleague, boss, and unknown • Representative Sample of 20, regular routine • Participants “called” at regular intervals by individual with one of the relationship types • Asked to share context

  13. Results, Mobile Phones

  14. Criticism, Mobile Phones • Bad: Value is not real • Participants were not receiving real phone calls based on their answers • Goal: Avoid interruptions • Questionnaire is an interruption • Good: • Context is more than location • Ideas for Configuration in Real Setting

  15. Privacy Tradeoffs • Value of Sharing vs. Privacy of Not Sharing • Control vs. Trust • Prevention vs. Detection • Configurability vs. Invisibility • Fidelity vs. Confidentiality • Fine vs. Coarse Grained Filtering

  16. Exercise 2: User Study • Same groups • Create a user study for the Professional Bus Tracking System • Try to determine if the solution uses the correct trade offs • Focus on usability of privacy, not on overall usability • 20 minutes

  17. Bibliography • http://www.tartanracing.org/ • Khalil, A. and Connelly, K. 2006. Context-aware telephony: privacy preferences and sharing patterns. In Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work (Banff, Alberta, Canada, November 04 - 08, 2006). CSCW '06. ACM, New York, NY, 469-478. • Tang, K. P., Keyani, P., Fogarty, J., and Hong, J. I. 2006. Putting people in their place: an anonymous and privacy-sensitive approach to collecting sensed data in location-based applications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Montreal, Quebec, Canada, April 22 - 27, 2006). R. Grinter, T. Rodden, P. Aoki, E. Cutrell, R. Jeffries, and G. Olson, Eds. CHI '06. ACM, New York, NY, 93-102. • Hong, J.I., J. Ng, and J.A. Landay. Privacy Risk Models for Designing Privacy-Sensitive Ubiquitous Computing Systems. In Proceedings of Designing Interactive Systems (DIS2004). Boston, MA. pp. 91-100 2004.

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