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Personal data vaults: a locus of control for personal data streams

Published:30 November 2010Publication History

ABSTRACT

The increasing ubiquity of the mobile phone is creating many opportunities for personal context sensing, and will result in massive databases of individuals' sensitive information incorporating locations, movements, images, text annotations, and even health data. In existing system architectures, users upload their raw (unprocessed or filtered) data streams directly to content-service providers and have little control over their data once they "opt-in".

We present Personal Data Vaults (PDVs), a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing a PDV gives users flexible and granular access control over data. To reduce the burden on users and improve usability, we explore three mechanisms for managing data policies: Granular ACL, Trace-audit and Rule Recommender. We have implemented a proof-of-concept PDV and evaluated it using real data traces collected from two personal participatory sensing applications.

References

  1. Freereversegeo. www.freereversegeo.com.Google ScholarGoogle Scholar
  2. Google health. https://www.google.com/health.Google ScholarGoogle Scholar
  3. Microsoft healthvault. http://www.healthvault.com.Google ScholarGoogle Scholar
  4. Mysql - couchdb performance comparison. http://metalelf0dev.blogspot.com/2008/09/mysql-couchdb-performance-comparison.html.Google ScholarGoogle Scholar
  5. oauth. http://oauth.net/.Google ScholarGoogle Scholar
  6. X.509. http://en.wikipedia.org/wiki/X.509.Google ScholarGoogle Scholar
  7. R. Baden, A. Bender, N. Spring, B. Bhattacharjee, and D. Starin. Persona: An online social network with user-defined privacy. In SIGCOMM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. M. Breunig, H. P. Kriegel, R. T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In ACM SIGMOD, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. Srivastava. Participatory sensing. In ACM Sensys WSW Workshop, 2006.Google ScholarGoogle Scholar
  10. C. Cornelius, A. Kapadia, D. Kotz, D. Peebles, M. Shin, and N. Triandopoulos. Anonysense: privacy-aware people-centric sensing. In MobiSys '08: Proceeding of the 6th international conference on Mobile systems, applications, and services, pages 211--224, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. R. Cceres, L. Cox, H. Lim, A. Shakimov, and A. Varshavsky. Virtual individual servers as privacy-preserving proxies for mobile devices. In Proc. of 1st ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D. K. D. Anthony and T. Henderson. Privacy in locationaware computing environments. In Pervasive Computing, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. P. Dutta, P. Aoki, N. Kumar, A. Mainwaring, C. Myers, W. Willett, and A. Woodruff. Common Sense: Participatory Urban Sensing Using a Network of Handheld Air Quality Monitors (demonstration). In Proc. SenSys, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. E. Miluzzo, N. D. Lane and A. Campbell. Cenceme injecting sensing presence into social networking applications. In Proc. of EuroSSC, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. G. H. et al. Physical, social and experiential knowledge in pervasive computing environments. In Pervasive Computing, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. F. Froehlich, M. Y. Chen, and et al. Myexperience: a system for in situ tracing and capturing of user feedback on mobile phones. In ACM MobiSys, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. R. K. Ganti, N. Pham, Y. Tsai, and T. F. Abdelzaher. Poolview: Stream privacy for grassroots participatory sensing. In Sensys, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. Gaonkar, J. Li, R. R. Choudhury, L. Cox, and A. Schmidt. Micro-blog: sharing and querying content through mobile phones and social participation. In Proceedings of MobiSys, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. S. Guha, K. Tang, and P. Francis. Noyb: Privacy in online social networks. In WOSP 08: Proceedings of the First Workshop on Online Social Networks, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Hicks, N. Ramanathan, D. Kim, M. Monibi, J. Selsky, M. Hansen, and D. Estrin. Andwellness: An open mobile system for activity and experience sampling. In Proc. of Wireless Health, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. B. Hoh and et al. Enhancing security and privacy in traffic-monitoring systems. In IEEE Pervasive Computing, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. B. Hoh, M. Gruteser, R. Herring, J. Ban, D. Work, J. Herrera, and et al. Virtual trip lines for distributed privacy-preserving traffic monitoring. In Proceeding of the 6th international conference on Mobile systems, applications, and services, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. J. I. Hong and J. A. Landay. An architecture for privacy-sensitive ubiquitous computing. In Proceeding of the 6th international conference on Mobile systems, applications, and services, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. J. Horey, M. M. Groat, S. Forrest, and F. Esponda. Anonymous data collection in sensor networks. In Proceedings of the 4th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. J. Kang, W. Welbourne, B. Stewart, and G. Borriello. Extracting places from traces of locations. In Mobile Computing and Communications Review, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. J. Krumm. Inference Attacks on Location Tracks. Lecture Notes in Computer Science, 4480:127, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. Y. Mun, S. Reddy, K. Shilton, N. Yau, and et al. Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In Mobisys, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. H. Nissenbaum. Privacy as contextual integrity. In Washington Law Review, 2004.Google ScholarGoogle Scholar
  29. A. Parker, S. Reddy, and et al. Network System Challenges in Selective Sharing and Verification for Personal, Social, and Urban-ScaleSensingApplications. In HotNets, 2006.Google ScholarGoogle Scholar
  30. J. Ryder, B. Longstaff, S. Reddy, and D. Estrin. Ambulation: A tool for monitoring mobility patterns over time using mobile phones. In Social Computing with Mobile Phones Workshop at IEEE SocialCom, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. Seong, J. Seo, M. Nasielski, D. Sengupta, S. Hangal, and et al. PrPl: A Decentralized Social Networking Infrastructure. In ACM Workshop on Mobile Cloud Computing and Services: Social Networks and Beyond, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. K. Shilton, J. Burke, D. Estrin, M. Hansen, R. Govindan, and J. Kang. Designing the personal data stream: Enabling participatory privacy in mobile personal sensing. In The 37th Research Conference on Communication, Information and Internet Policy (TPRC), 2009.Google ScholarGoogle Scholar
  33. A. Tootoonchian, S. Saroiu, Y. Ganjali, and A. Wolman. Lockr: Better privacy for social networks. In CoNEXT, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. C. Zhou, D. Frankowski, P. Ludford, S. Shekhar, and L. Terveen. Discovering personal gazetteers: an interactive clustering approach. In ACM GIS, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        Co-NEXT '10: Proceedings of the 6th International COnference
        November 2010
        349 pages
        ISBN:9781450304481
        DOI:10.1145/1921168

        Copyright © 2010 ACM

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        Publication History

        • Published: 30 November 2010

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