Technology and Privacy

2. Technology and Privacy Teresa Lunt Palo Alto Research Center In collaboration with Victoria Bellotti, Richard Chow, Glenn Durfee, Philippe Golle, Diana Smetters, Jessica Staddon, and others…

3. Hot Privacy Technology Research Voter Registration Name Street address … Gender ZIP code Date of Birth • Identity management and access control • Credentials, reputation, pseudonyms, unlinkability, phishing • Data privacy, data mining and inference control • Census data, medical data • Location privacy • Location-based cell-phone services, friend locator • Usable privacy • Privacy policy languages, automatic negotiation • Anonymous communication • Web-browsing, censorship resistant publishing, voting Gender ZIP code Date of Birth Cancer Type Patient Records

4. y x Private Computation Record i Index i Hot Privacy Technology Research • Privacy in ubiquitous computing environments • Sensors, RFID tags • Private information retrieval • Look-up of patents, medical conditions, DNA sequences • Private computations (secure function evaluation) • Millionaire problem, collaborative forecasting, intrusion detection • Search on encrypted data • Classification of encrypted emails, encrypted documents • And More… • Economics of privacy, policy, law, etc. f (x, y)

5. Technology is a Moving Target • Examples of current research at PARC • Ubiquitous Computing • Embedded everywhere, often with sensors • Context Aware Computing • Able to make inferences and act based on sensing • Social Computing • Interaction and collaboration online • Privacy and Security Research • Increased IT capabilities imply greater need for control

6. Enable sale of transformed data while providing privacy protection for individuals represented in the data PARC started research here but more work is needed Ubicomp & sensor data data source data source privacy appliance customer query Information storefront Privacy-preserving transformations and knowledge extraction data source Outsourced data Privacy Appliance Transaction data

7. Personal Privacy Appliance 3. She enables the mall to read her profile from her phone. No identity is needed Personal Privacy Appliance 1. Her shopping and other activities are reported to her PPA Profile Database 2. She synchs her phone with her profile

8. User Control of Profile Dissemination • Clothing • Colors: B • Size: 16 • Extra tall • Books • History • Mysteries • Music • Opera • Baroque • Children • Aged 3 and 5 Privacy software in the PDA can inform the user of the privacy consequences of releasing certain data Medium sensitive Not sensitive Very sensitive X X Stores could offer incentives to users to enable their profiles: coupons, discounts, … Users can disable portions of their profile when entering certain stores, friends houses, etc.

9. In-document Security Document appears different to people based on their access rights Property Address: 1234 Main St. Fence with adjacent property, 1234 Elm St. , is damaged. MortgageApplication Home Inspection Report Credit Report Natural Language understanding aids content analysis SSN: 123-45-6789 Social Security Number: 123456789 Property Address: 1234 Main St. Property Appraisal Police Report Atherton, CA Property has view of Golden Gate Bridge. …outside the apartment building… Property Address: 1234 Main St.

10. Technology Privacy A Never-Ending Story • Technology and privacy are often seen as opposing forces • Privacy advocates feel like Sisyphus versus his stone • But some technologists feel the same way • Both sides need to work together continuously

11. The internet of things: Toward distributed knowledge production David J. Phillips University of Toronto Faculty of Information Studies

12. Two scenarios • administrative efficiency • distributed knowledge production (smart mobs)

13. How does a mob get smart?

14. How does a mob get smart? • know the environment

15. How does a mob get smart? • know the environment • know each other

16. How does a mob get smart? • know the environment • know each other • live together in the environment

17. How does a mob get smart? • know the environment • know each other • live together in the environment • know the enemy

18. Distributed knowledge and policy making

19. Distributed knowledge and policy making • data protection principles

20. Distributed knowledge and policy making • data protection principles • industry organization

21. Distributed knowledge and policy making • data protection principles • industry organization • theory building • security vs privacy

22. Distributed knowledge and policy making • data protection principles • industry organization • theory building • security vs privacy • new questions for policy makers

23. David J. Phillips University of Toronto Faculty of Information Studies davidj.phillips@utoronto.ca

25. the internet of things:… well then,why not people? iankerr canada research chair in ethics, law & technology university of ottawa

26. ubiqcomp

27. ubiqcomp≠ UBIQCOMP

28. ubiqcomp

30.  ad hocnetwork

31. privacy ??

32. privacy by design

33. 1.

36. 2.

37. (re)design the conceptof privacy

40. oct 25/26/27

41. sccprivacy hierarchy personal (bodily)  territorial (property)  informational (core biographical)

42. information emanation

43. flir

44. (rfid)

45. sccprivacy hierarchy personal (bodily) •  territorial (property) •  informational(core biographical)

46. info ≠core biographical

47. privacy

48. reasonable expectations

49. ‘spatial privacy’ = {terr + info + …}

50. internet of ‘things’