Secure Evaluation of Private Linear Branching Programs with Medical Applications

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. Fin...

• Type: Conference
• Year: 2009
• Where: ESORICS
• Authors: Mauro Barni, Pierluigi Failla, Vladimir Kolesnikov, Riccardo Lazzeretti, Ahmad-Reza Sadeghi, Thomas Schneider
• Keywords: ESORICS 2009, Linear Branching Programs, Remote Diagnostic Proposal, Security Privacy, Subtle Security Weakness