Cryptology ePrint Archive: Report 2009/195

Secure Evaluation of Private Linear Branching Programs with Medical Applications

Mauro Barni and Pierluigi Failla and Vladimir Kolesnikov and Riccardo Lazzeretti and Ahmad-Reza Sadeghi and Thomas Schneider

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.

Category / Keywords: cryptographic protocols /

Publication Info: ESORICS 2009

Date: received 5 May 2009, last revised 29 Jun 2009

Contact author: thomas schneider at trust rub de

Available formats: PDF | BibTeX Citation

Note: This is the full version of the ESORICS 2009 paper.

Version: 20090629:121437 (All versions of this report)

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