Privacy preserving data mining

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

539 Scopus citations


In this paper we introduce the concept of privacy preserving data mining. In our model, two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. This problem has many practical and important applications, such as in medical research with confidential patient records. Data mining algorithms are usually complex, especially as the size of the input is measured in megabytes, if not gigabytes. A generic secure multi-party computation solution, based on evaluation of a circuit computing the algorithm on the entire input, is therefore of no practical use. We focus on the problem of decision tree learning and use ID3, a popular and widely used algorithm for this problem. We present a solution that is considerably more efficient than generic solutions. It demands very few rounds of communication and reasonable bandwidth. In our solution, each party performs by itself a computation of the same order as computing the ID3 algorithm for its own database. The results are then combined using efficient cryptographic protocols, whose overhead is only logarithmic in the number of transactions in the databases. We feel that our result is a substantial contribution, demonstrating that secure multi-party computation can be made practical, even for complex problems and large inputs.

Original languageEnglish
Title of host publicationAdvances in Cryptology - CRYPTO 2000 - 20th Annual International Cryptology Conference, Proceedings
EditorsMihir Bellare
PublisherSpringer Verlag
Number of pages19
ISBN (Print)9783540445982
StatePublished - 2000
Externally publishedYes
Event20th Annual International Cryptology Conference, CRYPTO 2000 - Santa Barbara, United States
Duration: 20 Aug 200024 Aug 2000

Publication series

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


Conference20th Annual International Cryptology Conference, CRYPTO 2000
Country/TerritoryUnited States
CitySanta Barbara

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.


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