16:198:500:07 Light Seminar: Readings in Differential Privacy

Seminar Description

In this light seminar / reading group, we will discuss a number of research papers on differential privacy. Students will take turns presenting papers. All students should read Cynthia Dwork's survey paper, which introduces the differential privacy concept and many of the results we will cover in the seminar.

Schedule

Papers

• "Differential Privacy: A Survey of Results," Everyone should read this paper, which surveys most of the other results.
  Cynthia Dwork,
  TAMC 2008, Springer LNCS 4978, pp. 1-19, 2008.

• What Can We Learn Privately?,
  Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam Smith,
  FOCS 2008.

• New Efficient Attacks on Statistical Disclosure Control Mechanisms,
  Cynthia Dwork and Sergey Yekhanin,
  Advances in Cryptology—CRYPTO 2008.

• A Learning Theory Approach to Non-Interactive Database Privacy Aleksander presents this.
  Avrim Blum, Katrina Ligett, and Aaron Roth,
  STOC 2008.

• Mechanism Design via Differential Privacy Darakshan presents this.
  Frank McSherry and Kunal Talwar,
  FOCS 2007, pp. 94-103, 2007.

• The Price of Privacy and the Limits of LP Decoding
  Cynthia Dwork, Frank McSherry, and Kunal Talwar
  39th Annual ACM Symposium on Theory of Computing—STOC 2007, pp. 85–94, 2007.

• Smooth Sensitivity and Sampling in Private Data Analysis Mangesh presents this.
  Kobbi Nissim, Sofya Raskhodnikova and Adam Smith
  39th Annual ACM Symposium on Theory of Computing—STOC 2007.

• Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release
  Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar
  26th Symposium on Principles of Database Systems—PODS 2007, pp. 273–282, 2007.

• Data Collection With Self-Enforcing Privacy
  Philippe Golle, Frank McSherry, and Ilya Mironov
  ACM Conference on Computer and Communications Security—ACM CCS 2006, pp. 69–78, 2006. Full version.

• Our Data, Ourselves: Privacy via Distributed Noise Generation Andre presents this.
  Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor
  Advances in Cryptology—Eurocrypt 2006, pp. 486–503, 2006.

• Calibrating Noise to Sensitivity in Private Data Analysis Geetha presents this.
  Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith
  3rd Theory of Cryptography Conference—TCC 2006, pp. 265–284, 2006.

• Toward Privacy in Public Databases
  Shuchi Chawla, Cynthia Dwork, Frank McSherry, Adam Smith, Hoeteck Wee
  2nd Theory of Cryptography Conference—TCC 2005, pp. 363–385, 2005.

• On the Utility of Privacy-Preserving Histograms
  Shuchi Chawla, Cynthia Dwork, Frank McSherry, Kunal Talwar
  21st Conference on Uncertainty in Artificial Intelligence—UAI 2005.

• Practical Privacy: The SuLQ Framework
  Avrim Blum, Cynthia Dwork, Frank McSherry, Kobbi Nissim
  24th Symposium on Principles of Database Systems—PODS 2005, pp. 128–138, 2005.

• Privacy-Preserving Data Mining in Vertically Partitioned Databases
  Cynthia Dwork, Kobbi Nissim
  Advances in Cryptology—CRYPTO 2004, pp. 528–544, 2004.

• Revealing Information While Preserving Privacy Gayatree presents this.
  Irit Dinur and Kobbi Nissim
  Proc. of PODS, 2003.

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  Last updated 11/5/08 by rebecca.wright (at) rutgers.edu

  Copyright © 2008 Rebecca N. Wright