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Privacy Preserving Data Mining within Anonymous Credential Systems

  • Aggelos Kiayias
  • Shouhuai Xu
  • Moti Yung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5229)

Abstract

Regular (non-private) data mining can be applied to manage and utilize accumulated transaction data. For example, the accumulated relative service time per user per month can be calculated given individual transaction data from which the user compliance with a service agreement can be determined and possibly billing can be processed. Nevertheless, due to user privacy concerns, cryptographic research developed transactions based on unlinkable anonymous credentials. Given the nature of anonymous credentials the ease of managing accumulated data (e.g., per user) is lost. To restore the possibility of management and accumulation of data it seems that a suitable form of privacy preserving data mining is needed. Indeed, privacy preserving data mining methods have been suggested for various protocols and interactions where individual data can be contributed in an encrypted form, but not within the context of anonymous credentials. Given our motivation we suggest a new notion of performing “privacy preserving data mining within the context of anonymous cryptographic credential systems,” so as to protect both the privacy of individually contributed data and the identity of their sources while revealing only what is needed. To instantiate our approach we focus on a primitive we call “data mining group signatures” (DMGS), where it is possible for a set of authorities to employ distributed quorum control for conducting privacy preserving data mining operations on a batch of transactions while preserving maximum possible anonymity. We define and model the new primitive and its security goals, we then present a construction and finally show its privacy and security properties. Along the way we build a methodology that safely combines multi-server protocols as sub-procedures in a more general setting.

Keywords

Random Oracle Public Input Mining Server Group Signature Scheme Anonymous Credential 
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 2008

Authors and Affiliations

  • Aggelos Kiayias
    • 1
  • Shouhuai Xu
    • 2
  • Moti Yung
    • 3
  1. 1.Computer Science and EngineeringUniversity of ConnecticutStorrsUSA
  2. 2.University of TexasSan AntonioUSA
  3. 3.Google Inc. and Computer ScienceColumbia UniversityNew York, NYUSA

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