Joshua Sunshine

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

Everywhere (duh!) Invisible Mobile Interoperable Context Aware Personal Multi-Agent

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)

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

Problem: Interruptions Caller doesnt know receivers context Solution: Reveal Context Location Activity Company Conversation

Problem: When will the next bus arrive? Tool: Cell phones Solution: Aggregate information from riders phones Send alerts to people waiting for a bus

Break up into two groups Make a list of privacy problems Come up with a solution that avoids or minimizes these problems 10 minutes

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

Hitchhiking Anonymous data collection Location is Computed on the Client Only the Client Device is Trusted Report Approval Restriction of Reports to Specific Locations

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

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

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

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

Khalil, A. and Connelly, K Context-aware telephony: privacy preferences and sharing patterns. In Proceedings of the th Anniversary Conference on Computer Supported Cooperative Work (Banff, Alberta, Canada, November , 2006). CSCW '06. ACM, New York, NY, Tang, K. P., Keyani, P., Fogarty, J., and Hong, J. I 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 , 2006). R. Grinter, T. Rodden, P. Aoki, E. Cutrell, R. Jeffries, and G. Olson, Eds. CHI '06. ACM, New York, NY, 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