Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining


  • Trung Duy Pham University of Canberra
  • Dat Tran University of Canberra
  • Wanli Ma University of Canberra




Ownership protection, Biomedical data, Watermarking, Data mining, Particle Swarm Optimization (PSO)


In the biomedical and healthcare fields, the ownership protection of the outsourced data is becoming a challenging issue in sharing the data between data owners and data mining experts to extract hidden knowledge and patterns. Watermarking has been proved as a right-protection mechanism that provides detectable evidence for the legal ownership of a shared dataset, without compromising its usability under a wide range of data mining for digital data in different formats such as audio, video, image, relational database, text and software. Time series biomedical data such as Electroencephalography (EEG) or Electrocardiography (ECG) is valuable and costly in healthcare, which need to have owner protection when sharing or transmission in data mining application. However, this issue related to kind of data has only been investigated in little previous research as its characteristics and requirements. This paper proposes an optimized watermarking scheme to protect ownership for biomedical and healthcare systems in data mining. To achieve the highest possible robustness without losing watermark transparency, Particle Swarm Optimization (PSO) technique is used to optimize quantization steps to find a suitable one. Experimental results on EEG data show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as noise addition, low-pass filtering, and re-sampling.




How to Cite

Pham, T. D., Tran, D., & Ma, W. (2017). Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining. Australasian Journal of Information Systems, 21. https://doi.org/10.3127/ajis.v21i0.1541



Selected Papers from the Australasian Conference on Data Mining (AusDM)