Skip to main content
Article thumbnail
Location of Repository

Secure evaluation of private linear branching programs with medical applications

By Mauro Barni, Pierluigi Failla, Vladimir Kolesnikov, Riccardo Lazzeretti, Ahmad-reza Sadeghi and Thomas Schneider


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

Year: 2009
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • http://www.informatik.tu-darms... (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.