DIMACS/PORTIA Workshop on Privacy-Preserving Data Mining

March 15 - 16, 2004
DIMACS Center, CoRE Building, Rutgers University, Piscataway, NJ

Organizers:

Cynthia Dwork, Microsoft, dwork at microsoft.com

Benny Pinkas, HP Labs, benny.pinkas at hp.com

Rebecca Wright, Stevens Institute of Technology, rwright at cs.stevens-tech.edu

Papers and Slides:

• Steven Bellovin, AT & T Research
  Privacy-Enhanced Searches Using Encrypted Bloom Filters

• Shuchi Chawla, CMU
  From Idiosyncratic to Stereotypical: Toward Privacy in Public Databases

• Kevin Du, Syracuse Unversity
  When does the randomization fail to protect privacy? (pdf file)
  When does the randomization fail to protect privacy? (ppt file)

• Kevin Du, Syracuse Unversity
  Hillol Karagupta, University of Maryland
  Poorvi Vora, George Washington Unversity
  Some open questions: aggregation and privacy protection
  Coding, information theory and signal processing

• Petros Drineas, Rensselaer Polytechnic Institute
  Computing sketches of matrices efficiently and privacy preserving data mining

• Mike Freedman, NYU
  Efficient Private Matching and Set Intersection

• Yuval Ishai, Technion
  Extending Oblivious Transfers Efficiently (pdf file)
  Extending Oblivious Transfers Efficiently (ppt file)

• Eyal Kushilevitz, Technion
  Amortized PIR via Batch Codes

• Yehuda Lindell, IBM
  Open Questions & Research Areas in Privacy-Preserving Data Mining

• Helen Nissenbaum, NYU
  Privacy as Contextual Integrity

• Emmanuel Pontikakis, University of Patras
  An Experimental Study of Association Rule Hiding Technique

• Poorvi Vora, George Washington Unversity
  A Model for Data Revelation

• Tal Zarsky, Yale Law School
  Data Mining and Information Privacy - New Problems and the Search For Solutions (pdf file)
  Data Mining and Information Privacy - New Problems and the Search For Solutions (ppt file)

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