Understanding privacy risk of publishing decision trees

Zutao Zhu, Wenliang Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Publishing decision trees can provide enormous benefits to the society. Meanwhile, it is widely believed that publishing decision trees can pose a potential risk to privacy. However, there is not much investigation on the privacy consequence of publishing decision trees. To understand this problem, we need to quantitatively measure privacy risk. Based on the well-established maximum entropy theory, we have developed a systematic method to quantify privacy risks when decision trees are published. Our method converts the knowledge embedded in decision trees into equations and inequalities (called constraints), and then uses nonlinear programming tool to conduct maximum entropy estimate. The estimate results are then used to quantify privacy. We have conducted experiments to evaluate the effectiveness and performance of our method.

Original languageEnglish (US)
Title of host publicationData and Applications Security and Privacy XXIV - 24th Annual IFIP WG 11.3 Working Conference, Proceedings
Pages33-48
Number of pages16
DOIs
StatePublished - Oct 29 2010
Event24th Annual IFIP WG 11.3 Working Conference on Data and Applications Security and Privacy - Rome, Italy
Duration: Jun 21 2010Jun 21 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6166 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other24th Annual IFIP WG 11.3 Working Conference on Data and Applications Security and Privacy
CountryItaly
CityRome
Period6/21/106/21/10

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhu, Z., & Du, W. (2010). Understanding privacy risk of publishing decision trees. In Data and Applications Security and Privacy XXIV - 24th Annual IFIP WG 11.3 Working Conference, Proceedings (pp. 33-48). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6166 LNCS). https://doi.org/10.1007/978-3-642-13739-6_3