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.

Secure Evaluation of Private Linear Branching Programs with Medical Applications / Barni, M; Failla, P; Kolesnikov, V; Lazzeretti, Riccardo; Sadeghi, A; Schneider, T.. - 5789:(2009), pp. 424-439. (Intervento presentato al convegno 14th European Symposium on Research in Computer Security, ESORICS 2009 tenutosi a Saint-Malo; France) [10.1007/978-3-642-04444-1_26].

Secure Evaluation of Private Linear Branching Programs with Medical Applications

LAZZERETTI, RICCARDO;
2009

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.
2009
14th European Symposium on Research in Computer Security, ESORICS 2009
Signal Processing in the encrypted domain; homomorphic encryption, garbled circuits
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Secure Evaluation of Private Linear Branching Programs with Medical Applications / Barni, M; Failla, P; Kolesnikov, V; Lazzeretti, Riccardo; Sadeghi, A; Schneider, T.. - 5789:(2009), pp. 424-439. (Intervento presentato al convegno 14th European Symposium on Research in Computer Security, ESORICS 2009 tenutosi a Saint-Malo; France) [10.1007/978-3-642-04444-1_26].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/967142
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