Barni, Mauro ; Failla, Pierluigi ; Lazzeretti, Riccardo ; Sadeghi, Ahmad-Reza ; Schneider, Thomas (2011)
Privacy-Preserving ECG Classification with Branching Programs and Neural Networks.
In: IEEE Transactions on Information Forensics and Security (TIFS), 6 (2)
doi: 10.1109/TIFS.2011.2108650
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Privacy protection is a crucial problem in many biomedical signal processing applications. For this reason, particular attention has been given to the use of secure multiparty computation techniques for processing biomedical signals, whereby nontrusted parties are able to manipulate the signals although they are encrypted. This paper focuses on the development of a privacy preserving automatic diagnosis system whereby a remote server classifies a biomedical signal provided by the client without getting any information about the signal itself and the final result of the classification. Specifically, we present and compare two methods for the secure classification of electrocardiogram (ECG) signals: the former based on linear branching programs (a particular kind of decision tree) and the latter relying on neural networks. The paper deals with all the requirements and difficulties related to working with data that must stay encrypted during all the computation steps, including the necessity of working with fixed point arithmetic with no truncation while guaranteeing the same performance of a floating point implementation in the plain domain. A highly efficient version of the underlying cryptographic primitives is used, ensuring a good efficiency of the two proposed methods, from both a communication and computational complexity perspectives. The proposed systems prove that carrying out complex tasks like ECG classification in the encrypted domain efficiently is indeed possible in the semihonest model, paving the way to interesting future applications wherein privacy of signal owners is protected by applying high security standards.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2011 |
| Autor(en): | Barni, Mauro ; Failla, Pierluigi ; Lazzeretti, Riccardo ; Sadeghi, Ahmad-Reza ; Schneider, Thomas |
| Art des Eintrags: | Bibliographie |
| Titel: | Privacy-Preserving ECG Classification with Branching Programs and Neural Networks |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2011 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Transactions on Information Forensics and Security (TIFS) |
| Jahrgang/Volume einer Zeitschrift: | 6 |
| (Heft-)Nummer: | 2 |
| DOI: | 10.1109/TIFS.2011.2108650 |
| URL / URN: | https://encrypto.de/papers/BFLSS11.pdf |
| Kurzbeschreibung (Abstract): | Privacy protection is a crucial problem in many biomedical signal processing applications. For this reason, particular attention has been given to the use of secure multiparty computation techniques for processing biomedical signals, whereby nontrusted parties are able to manipulate the signals although they are encrypted. This paper focuses on the development of a privacy preserving automatic diagnosis system whereby a remote server classifies a biomedical signal provided by the client without getting any information about the signal itself and the final result of the classification. Specifically, we present and compare two methods for the secure classification of electrocardiogram (ECG) signals: the former based on linear branching programs (a particular kind of decision tree) and the latter relying on neural networks. The paper deals with all the requirements and difficulties related to working with data that must stay encrypted during all the computation steps, including the necessity of working with fixed point arithmetic with no truncation while guaranteeing the same performance of a floating point implementation in the plain domain. A highly efficient version of the underlying cryptographic primitives is used, ensuring a good efficiency of the two proposed methods, from both a communication and computational complexity perspectives. The proposed systems prove that carrying out complex tasks like ECG classification in the encrypted domain efficiently is indeed possible in the semihonest model, paving the way to interesting future applications wherein privacy of signal owners is protected by applying high security standards. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt 20 Fachbereich Informatik > EC SPRIDE 20 Fachbereich Informatik > EC SPRIDE > Engineering Cryptographic Protocols (am 01.03.18 aufgegangen in Praktische Kryptographie und Privatheit) |
| Hinterlegungsdatum: | 25 Jun 2012 13:13 |
| Letzte Änderung: | 08 Aug 2024 12:23 |
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