The Confounding Problem of Private Data Release (Invited Talk)

Author Graham Cormode



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Graham Cormode

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Graham Cormode. The Confounding Problem of Private Data Release (Invited Talk). In 18th International Conference on Database Theory (ICDT 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 31, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015) https://doi.org/10.4230/LIPIcs.ICDT.2015.1

Abstract

The demands to make data available are growing ever louder, including open data initiatives and "data monetization". But the problem of doing so without disclosing confidential information is a subtle and difficult one. Is "private data release" an oxymoron? This paper (accompanying an invited talk) aims to delve into the motivations of data release, explore the challenges, and outline some of the current statistical approaches developed in response to this confounding problem.

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Keywords
  • privacy
  • anonymization
  • data release

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References

  1. Graham Cormode. Personal privacy vs population privacy: Learning to attack anonymization. In ACM SIGKDD, August 2011. Google Scholar
  2. Graham Cormode, Magda Procopiuc, Divesh Srivastava, and Thanh Tran. Differentially private publication of sparse data. In International Conference on Database Theory, 2012. Google Scholar
  3. Graham Cormode, Magda Procopiuc, Divesh Srivastava, Xiaokui Xiao, and Jun Zhang. Privbayes: Private data release via bayesian networks. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2014. Google Scholar
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  9. L. Sweeney. Simple demographics often identify people uniquely. Technical Report Data Privacy Working Paper 3, Carnegie Mellon University, 2000. Google Scholar
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