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Privacy Preserving Classification of ECG Signals in Mobile e-Health Applications

  • Chapter
Medical Data Privacy Handbook

Abstract

Privacy protection is an emerging problem in mobile Health applications. On one hand, cloud services enable to store personal medical data, making them always available, and providing preliminary analysis on them, on the other hand, storing personal health data entails serious threats to users privacy. Privacy preserving solutions, such as Secure Multi-Party Computation techniques, give to non-trusted parties the opportunity of processing biomedical signals while encrypted. This chapter focuses on the development of a privacy preserving automatic diagnosis system whereby a remote server classifies an ElectroCardioGram (ECG) signal provided by the client without obtaining neither any information about the signal itself, nor the final result of the classification. Specifically, we present and compare three secure implementations of ECG classifiers: Linear Branching Programs (a particular kind of decision tree) with Quadratic Discriminant Functions, Linear Branching Programs with Linear Discriminant Functions and Neural Networks. Moreover we describe a protocol that permits to evaluate the quality of an encrypted ECG. The chapter provides a signal processing analysis aiming at satisfying both accuracy and complexity requirements. The described systems prove that carrying out complex tasks like ECG classification in the encrypted domain is indeed possible in the semi-honest model, paving the way to interesting future applications wherein privacy of signal owners is protected by applying high security standards.

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Notes

  1. 1.

    When only two players are involved, SMPC reduces to Secure Two Party Computation (STPC).

  2. 2.

    The garbling of a d-input gate requires the computation of 2d − 1 Hash functions and the transmission of (2d − 1)t bits, while gate evaluation requires the computation of a Hash function with probability \(1 - 1/2^{d}\).

  3. 3.

    Tests were performed in 2009. Anyway these values are still widely used today.

  4. 4.

    http://www.shamus.ie.

  5. 5.

    This kind of Neural Network is often called NN with fired output.

  6. 6.

    NN network training requires a large dataset, hence authors had the necessity to choose a bigger training set than the one used for the LBP implementation, where the training and test dataset size have been chosen according to [21].

  7. 7.

    Many other NNs have been trained by changing the number of neurons and the activation functions, as well as the training method, before choosing the NN described here. See [7] for details.

  8. 8.

    Of course the size of the decision tree may change as well.

  9. 9.

    The computation of the square value needs interaction with the decryption key owner.

  10. 10.

    This choice is motivated by the fact that 30 s are sufficient for SNR evaluation and do not introduce a big delay in further computation.

  11. 11.

    The communication complexity estimate is performed by considering T = 1248 (instead of T = 1024, as in the original paper), garbled row reduction and OT precomputation.

  12. 12.

    http://www.physionet.org/physiobank/database/mitdb/.

  13. 13.

    http://www.physionet.org/physiobank/database/nstdb/.

  14. 14.

    We considered T = 1248, garbled row reduction and OT precomputation.

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Acknowledgements

We would like to thank the co-authors of the original papers, i.e., Pierluigi Failla, Jorge Guajardo, Vladimir Kolesnikov, Annika Paus, Ahmad-Reza Sadeghi, Thomas Schneider (in alphabetical order), for the important contribution provided to the research in this interesting field.

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Authors and Affiliations

  1. Department of Information Engineering & Mathematics, University of Siena, Siena, Italy

    Riccardo Lazzeretti & Mauro Barni

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  1. Riccardo Lazzeretti
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Correspondence to Riccardo Lazzeretti .

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  1. IBM Research - Ireland, Mulhuddart, Dublin, Ireland

    Aris Gkoulalas-Divanis

  2. Cardiff University, Cardiff, United Kingdom

    Grigorios Loukides

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Lazzeretti, R., Barni, M. (2015). Privacy Preserving Classification of ECG Signals in Mobile e-Health Applications. In: Gkoulalas-Divanis, A., Loukides, G. (eds) Medical Data Privacy Handbook. Springer, Cham. https://doi.org/10.1007/978-3-319-23633-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-23633-9_22

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