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Secure Evaluation of Private Linear Branching Programs with Medical Applications

  • Mauro Barni
  • Pierluigi Failla
  • Vladimir Kolesnikov
  • Riccardo Lazzeretti
  • Ahmad-Reza Sadeghi
  • Thomas Schneider
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5789)

Abstract

Diagnostic and classification algorithms play an important role in data analysis, with applications in areas such as health care, fault diagnostics, or benchmarking. Branching programs (BP) is a popular representation model for describing the underlying classification/diagnostics algorithms. Typical application scenarios involve a client who provides data and a service provider (server) whose diagnostic program is run on client’s data. Both parties need to keep their inputs private.

We present new, more efficient privacy-protecting protocols for remote evaluation of such classification/diagnostic programs. In addition to efficiency improvements, we generalize previous solutions – we securely evaluate private linear branching programs (LBP), a useful generalization of BP that we introduce. We show practicality of our solutions: we apply our protocols to the privacy-preserving classification of medical ElectroCardioGram (ECG) signals and present implementation results. Finally, we discover and fix a subtle security weakness of the most recent remote diagnostic proposal, which allowed malicious clients to learn partial information about the program.

Keywords

Attribute Vector Decision Node Secure Evaluation Homomorphic Encryption Oblivious Transfer 
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 2009

Authors and Affiliations

  • Mauro Barni
    • 1
  • Pierluigi Failla
    • 1
  • Vladimir Kolesnikov
    • 2
  • Riccardo Lazzeretti
    • 1
  • Ahmad-Reza Sadeghi
    • 3
  • Thomas Schneider
    • 3
  1. 1.Department of Information EngineeringUniversity of SienaItaly
  2. 2.Bell LaboratoriesUSA
  3. 3.Horst Görtz Institute for IT-SecurityRuhr-University BochumGermany

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