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Distributed Data Mining Protocols for Privacy: A Review of Some Recent Results

  • Rebecca N. Wright
  • Zhiqiang Yang
  • Sheng Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4074)

Abstract

With the rapid advance of the Internet, a large amount of sensitive data is collected, stored, and processed by different parties. Data mining is a powerful tool that can extract knowledge from large amounts of data. Generally, data mining requires that data be collected into a central site. However, privacy concerns may prevent different parties from sharing their data with others. Cryptography provides extremely powerful tools which enable data sharing while protecting data privacy.

In this paper, we briefly survey four recently proposed cryptographic techniques for protecting data privacy in distributed settings. First, we describe a privacy-preserving technique for learning Bayesian networks from a dataset vertically partitioned between two parties. Then, we describe three privacy-preserving data mining techniques in a fully distributed setting where each customer holds a single data record of the database.

Keywords

Bayesian Network Cryptographic Technique Secure Multiparty Computation Malicious Adversary Privacy Preserve Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rebecca N. Wright
    • 1
  • Zhiqiang Yang
    • 1
  • Sheng Zhong
    • 2
  1. 1.Department of Computer ScienceStevens Institute of TechnologyHobokenUSA
  2. 2.Department of Computer Science & EngineeringState University of New York at BuffaloBuffaloUSA

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