An intelligent learning-based watermarking scheme for outsourced biomedical time series data

Dat Tran, Wanli Ma, Duy Pham

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

Abstract

Sharing outsourced data between owners and data mining experts is becoming a challenging issue in biomedical and healthcare fields. Watermarking has been proved as a right-protection mechanism that can provide detectable evidence for the legal ownership of a shared dataset, without compromising its usability. However, the main disadvantage of these conventional techniques is unintelligent, rule-based and they do not directly deal with the data synchronization. Therefore, decoding performance reduces significantly when the watermarked data is transmitted through a real communication channel. This paper proposes an intelligent learning-based watermark scheme for outsourced biomedical time series data. The scheme carries out embedding of watermark data based on modifying mean modulation relationship of approximation coefficients in wavelet domain. In addition, the correlation between modified frequency coefficients and the watermark sequence in wavelet domain is intelligently learnt by a machine learning algorithm. The watermark can be effectively retrieved using this learning algorithm. Experimental results on electroencephalography (EEG) data with support vector data description (SVDD) learning show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as random cropping, noise addition, low-pass filtering, and resampling.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4408-4415
Number of pages8
Volume2017-May
ISBN (Electronic)9781509061815
ISBN (Print)9781509061839
DOIs
Publication statusPublished - 30 Jun 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period14/05/1719/05/17

Fingerprint

Watermarking
Learning algorithms
Time series
Data description
Electroencephalography
Data mining
Decoding
Learning systems
Synchronization
Signal processing
Modulation

Cite this

Tran, D., Ma, W., & Pham, D. (2017). An intelligent learning-based watermarking scheme for outsourced biomedical time series data. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (Vol. 2017-May, pp. 4408-4415). [7966414] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2017.7966414
Tran, Dat ; Ma, Wanli ; Pham, Duy. / An intelligent learning-based watermarking scheme for outsourced biomedical time series data. 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 4408-4415 (Proceedings of the International Joint Conference on Neural Networks).
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abstract = "Sharing outsourced data between owners and data mining experts is becoming a challenging issue in biomedical and healthcare fields. Watermarking has been proved as a right-protection mechanism that can provide detectable evidence for the legal ownership of a shared dataset, without compromising its usability. However, the main disadvantage of these conventional techniques is unintelligent, rule-based and they do not directly deal with the data synchronization. Therefore, decoding performance reduces significantly when the watermarked data is transmitted through a real communication channel. This paper proposes an intelligent learning-based watermark scheme for outsourced biomedical time series data. The scheme carries out embedding of watermark data based on modifying mean modulation relationship of approximation coefficients in wavelet domain. In addition, the correlation between modified frequency coefficients and the watermark sequence in wavelet domain is intelligently learnt by a machine learning algorithm. The watermark can be effectively retrieved using this learning algorithm. Experimental results on electroencephalography (EEG) data with support vector data description (SVDD) learning show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as random cropping, noise addition, low-pass filtering, and resampling.",
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Tran, D, Ma, W & Pham, D 2017, An intelligent learning-based watermarking scheme for outsourced biomedical time series data. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. vol. 2017-May, 7966414, Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 4408-4415, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 14/05/17. https://doi.org/10.1109/IJCNN.2017.7966414

An intelligent learning-based watermarking scheme for outsourced biomedical time series data. / Tran, Dat; Ma, Wanli; Pham, Duy.

2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 4408-4415 7966414 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Tran D, Ma W, Pham D. An intelligent learning-based watermarking scheme for outsourced biomedical time series data. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May. USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 4408-4415. 7966414. (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2017.7966414