Ashima Anand

Bio: Ashima Anand is an academic researcher from National Institute of Technology, Patna. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 4, co-authored 12 publications receiving 122 citations. Previous affiliations of Ashima Anand include Thapar University.

Watermarking techniques for medical data authentication: a survey

Abstract: With the widespread growth of medical images and improved communication and computer technologies in recent years, authenticity of the images has been a serious issue for E-health applications. In order to this, various notable watermarking techniques are developed by potential researchers. However, those techniques are unable to solve many issues that are necessary to be measured in future investigations. This paper surveys various watermarking techniques in medical domain. Along with the survey, general concepts of watermarking, major characteristics, recent applications, concepts of embedding and recovery process of watermark, and the summary of various techniques (in tabular form) are highlighted in brief. Further, major issues associated with medical image watermarking are also discussed to find out research directions for fledgling researchers and developers.

Compression-Then-Encryption-Based Secure Watermarking Technique for Smart Healthcare System

Abstract: The smart healthcare system is an electronic patient records (EPR) sharing system, which significantly helps sharing of EPR data and provides appropriate medical assistance for the patients and a more suitable platform for the potential researchers. However, the security of EPR data is still a major issue in such systems. In this paper, we develop a compression-then-encryption-based dual watermarking to protect the EPR data for the healthcare system, which produces several significant features. Experiments conducted on a large set of medical data indicate the capability of our proposed method for smart healthcare. Finally, when compared with the existing technique, the proposed work offers better performance in terms of robustness and security.

Joint Watermarking-Encryption-ECC for Patient Record Security in Wavelet Domain

Abstract: Maintaining electronic patient records is the primary role for the fruitful implementation of the telehealth services in urban areas to enhance the quality of healthcare. These records frequently shared in-between professionals and healthcare centers so as to develop efficient tool(s) for accurate diagnosis purpose. However, protection of these records is a challenging task in the current era. In this article, we develop a joint watermarking-encryption- error correcting code (ECC) technique for the purpose of securing patient records. The suggested method involves three major steps as follows: 1) watermarks generation and scrambling, 2) encryption of cover and generated watermark via Paillier cryptosystem, and 3) imperceptibly embedding and robustly recovery/extraction of the watermarks. Experimental results indicate that the proposed technique offers high robustness and fulfills all watermarking needs for telehealth applications in terms of imperceptibility, robustness, capacity, and security. Furthermore, the proposed method achieves better robustness against varying attacks, and outperforms former techniques.

A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

Abstract: In a telemedicine diagnosis system, the emergence of 3D imaging enables doctors to make clearer judgments, and its accuracy also directly affects doctors’ diagnosis of the disease. In order to ensure the safe transmission and storage of medical data, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper. The proposed algorithm employs the principal component analysis (PCA) transform to reduce the data dimension, which can minimize the error between the extracted components and the original data in the mean square sense. Especially, this algorithm helps to create a bacterial foraging model based on particle swarm optimization (BF-PSO), by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding, thereby achieving the optimal balance between embedding capacity and imperceptibility. A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

Compression-Then-Encryption-Based Secure Watermarking Technique for Smart Healthcare System

Abstract: The smart healthcare system is an electronic patient records (EPR) sharing system, which significantly helps sharing of EPR data and provides appropriate medical assistance for the patients and a more suitable platform for the potential researchers. However, the security of EPR data is still a major issue in such systems. In this paper, we develop a compression-then-encryption-based dual watermarking to protect the EPR data for the healthcare system, which produces several significant features. Experiments conducted on a large set of medical data indicate the capability of our proposed method for smart healthcare. Finally, when compared with the existing technique, the proposed work offers better performance in terms of robustness and security.

Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring

Abstract: IoT devices generate massive amounts of biomedical data with increased digitalization and development of the state-of-the-art automated clinical data collection systems. When combined with advanced machine learning algorithms, the big data could be useful to improve the health systems for decision-making, diagnosis, and treatment. Mental healthcare is also attracting attention, since most medical problems can be associated with mental states. Affective computing is among the emerging biomedical informatics fields for automatically monitoring a person’s mental state in ambulatory environments by using physiological and physical signals. However, although affective computing applications are promising to improve our daily lives, before analyzing physiological signals, privacy issues and concerns need to be dealt with. Federated learning is a promising candidate for developing high-performance models while preserving the privacy of individuals. It is a privacy protection solution that stores model parameters instead of the data itself and abides by the data protection laws such as EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). We applied federated learning to heart activity data collected with smart bands for stress-level monitoring in different events. We achieved encouraging results for using federated learning in IoT-based wearable biomedical monitoring systems by preserving the privacy of the data.