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WRAP UP

WRAP UP. Joan Feigenbaum http://www.cs.yale.edu/homes/jf PORTIA Project Site Visit Stanford CA, May 12-13, 2005. Benefits of a Large-ITR Grant. Very high levels of Inter-institutional collaboration Inter-disciplinarity Visibility and outreach Integration of research and education

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WRAP UP

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  1. WRAP UP Joan Feigenbaum http://www.cs.yale.edu/homes/jf PORTIA Project Site Visit Stanford CA, May 12-13, 2005

  2. Benefits of a Large-ITR Grant Very high levels of • Inter-institutional collaboration • Inter-disciplinarity • Visibility and outreach • Integration of research and education • Public service by PIs

  3. Accomplishments Reported Today RW: PPDM and MDS algorithmics AB: Policy specification and enforcement DB: Browser-based ID protection HG: Retaining control of outsourced data JF: Highly collaborative education and outreach SF: Novel data representations AS: Domain-specific DB challenges HN: Novel conceptual framework

  4. Other Highlights of the First 1.5 Years • Search of access-controlled content • Economic analysis of “trusted platforms” • Provably secure query auditing • Privacy in public databases • BCC privacy violations in encrypted email • Cybercrime and cyberpolicing • The world changed (e.g., wrt spam, NGSCB).   

  5. The Powerful are Pessimistic • “You already have zero privacy. Get over it!” – Scott McNealy, 1999 • Microsoft Faculty Summit, August 2004 • Rick Rashid: People don’t even know what “privacy” means. • Bill Gates: Things are going to get worse. Google, Microsoft, and Yahoo! are all trying “to personalize search.”

  6. Sample Highlights of theNext 3.5 Years (1) • Work with more user communities on PPDM. • Genetics researchers (see Schäffer’s White Paper) • We could use help from NSF on this! • Deal with adversarial behavior in massive-graph computations and massive-matrix computations. • Computational realization of contextual integrity (or proof that there is none) • Experimental analysis of “public-records” policies

  7. Sample Highlights of theNext 3.5 Years (2) • Privacy-preserving data cleaning • Privacy-respecting personalized search • Policy-driven search in a social network • Enterprise-wide policy-driven search • Countering emerging threats to identity protection (e.g., bot-nets) • Compliance-testing tools • Health data: HIPAA • Financial data: SB1386,Sarbanes Oxley

  8. Thank You for Your Attention THE END

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