Compression-Then-Encryption-Based Secure Watermarking Technique for Smart Healthcare System
TL;DR: A compression-then-encryption-based dual watermarking to protect the EPR data for the healthcare system, which produces several significant features and offers better performance in terms of robustness and security.
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.
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TL;DR: In this article, the role of deep learning-based echocardiography in the diagnosis and evaluation of the effects of routine anti-heart-failure Western medicines was investigated in elderly patients with acute left heart failure (ALHF).
Abstract: Objective. The role of deep learning-based echocardiography in the diagnosis and evaluation of the effects of routine anti-heart-failure Western medicines was investigated in elderly patients with acute left heart failure (ALHF). Methods. A total of 80 elderly patients with ALHF admitted to Affiliated Hangzhou First People’s Hospital from August 2017 to February 2019 were selected as the research objects, and they were divided randomly into a control group and an observation group, with 40 cases in each group. Then, a deep convolutional neural network (DCNN) algorithm model was established, and image preprocessing was carried out. The binarized threshold segmentation was used for denoising, and the image was for illumination processing to balance the overall brightness of the image and increase the usable data of the model, so as to reduce the interference of subsequent feature extraction. Finally, the detailed module of deep convolutional layer network algorithm was realized. Besides, the patients from the control group were given routine echocardiography, and the observation group underwent echocardiography based on deep learning algorithm. Moreover, the hospitalization status of patients from the two groups was observed and recorded, including mortality rate, rehospitalization rate, average length of hospitalization, and hospitalization expenses. The diagnostic accuracy of the two examination methods was compared, and the electrocardiogram (ECG) and echocardiographic parameters as well as patients’ quality of life were recorded in both groups at the basic state and 5 months after drug treatment. Results. After comparison, the rehospitalization rate and mortality rate of the observation group were lower than the rates of the control group, but the diagnostic accuracy was higher than that of the control group. However, the difference between the two groups of patients was not statistically marked ( ). The length and expenses of hospitalization of the observation group were both less than those of the control group. The specificity, sensitivity, and accuracy of the examination methods in the observation group were higher than those of the control group, and the differences were statistically marked ( ). There was a statistically great difference between the interventricular delay (IVD) of the echocardiographic parameters of patients from the two groups at the basic state and the left ventricular electromechanical delay (LVEMD) parameter values after 5 months of treatment ( ), but there was no significant difference in the other parameters. After treatment, the quality of life of patients from the two groups was improved, while the observation group was more marked than the control group ( ). Conclusion. Echocardiography based on deep learning algorithm had high diagnostic accuracy and could reduce the possibility of cardiovascular events in patients with heart failure, so as to decrease the mortality rate and diagnosis and treatment costs. Moreover, it had an obvious diagnostic effect, which was conducive to the timely detection and treatment of clinical diseases.
6 citations
06 Jul 2021
TL;DR: In this paper, a robust X-ray image watermarking is proposed by using Non-Subsampled Contourlet Transform (NSCT) and Multiresolution Singular Value Decomposition (MSVD).
Abstract: Medical data transmission and sharing, especially during this COVID-19 pandemic period, on the open channel have become more important for remote diagnosis and treatment purpose. However, the alteration and unauthorized distribution of image data has become easier, and thus the big issue of copy-protection and ownership conflicts has attracted more attention for healthcare research community. Further, large amount of confidential and personal medical records is often stored on cloud environments. However, outsourcing medical data possibly brings the great security and privacy issue, since the confidential records are shared to the third party. In this paper, a robust X-Ray image watermarking is proposed by using Non-Subsampled Contourlet Transform (NSCT) and Multiresolution Singular Value Decomposition (MSVD). For watermark embedding, the maximum entropy component of X-Ray carrier image is firstly decomposed using NSCT. Then, low and high frequency details of carrier and mark image is obtained using MSVD. Further, conceal the watermark detail through modifying the detail of carrier image via the suitable factor. Finally, Shamir's (k, n) secret sharing algorithm is employed to obtain secure marked carrier image. Objective evaluations on 200 X-Ray images of COVID-19 patients demonstrate that the proposed algorithm has not only an excellent invisibility but a strong robustness against the various attacks. The results also show that our algorithm outperforms the related image watermarking algorithms, since it is also suitable for applications in the multi-cloud.
5 citations
TL;DR: The thirteen papers in this special section focus on social media data analytics with deep intelligence, which aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data.
Abstract: The thirteen papers in this special section focus on social media data analytics with deep intelligence. Big cross-model social media data analytics with deep intelligence aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data. The addressed topic span from the range of human action recognition to affective computing, disaster detection, classification, retrieval, clustering, vehicle reidentification, and data security.
5 citations
TL;DR: Wang et al. as mentioned in this paper proposed a verifiable keyword search scheme supporting sensitive information hiding for the cloud-based healthcare sharing system, where sensitive information is encrypted, while other contents in EMR can be shared among users.
Abstract: With the integration of the healthcare system, Internet of Things, and cloud storage service, more and more medical institutions upload their electronic medical records (EMRs) to the cloud to reduce the local storage burden and realize data sharing among external researchers. To secure the sensitive information, EMRs usually should be encrypted before being stored on the cloud. However, the existing searchable encryption schemes that encrypt the entire EMRs can hide the sensitive information, but this results in the shared EMRs being unable to be used by researchers. In addition, if the queried and extracted EMRs are incorrect, it will lead to misdiagnosis and even endanger the patient’s life. In order to solve the aforementioned problems, in this article, we propose a verifiable keyword search scheme supporting sensitive information hiding for the cloud-based healthcare sharing system. The sensitive information is encrypted, while other contents in EMR can be shared among users in this scheme. Doctors and researchers can quickly perform search operations based on keywords to extract the EMRs they require. This time complexity is $O(n)$ $n$ $\lbrace O(n^{\prime }),O(N^{\prime })\rbrace$ $n^{\prime }$ $N^{\prime }$
5 citations
14 Dec 2020
TL;DR: In this article, the authors used simulated annealing heuristic search algorithm to find the best scaling factor to reach more robust, transparent, high data capacity and resistant watermarking method in medical images.
Abstract: Nowadays, multimedia elements such as image, video, animation, audio, and software can be distributed in a short time anywhere in the world using internet technology. Content owners are concerned about copyright protection and authentication. Watermarking is one of the method to protect patient information in medical images such as magnetic resonance and ultrasound imaging. In the literature, there are several methods proposed using both frequency-domain (etc. digital cosine transforms (DCT), digital wavelet transforms (DWT), digital Fourier transforms (DFT), digital radon transform (DRT)) and spatial domain (least significant bits (LSB)) methods. Mostly digital wavelet transform based watermarking methods give very promising results after common attacks. In the DWT algorithm, scaling factor is used in embedding and extraction. A scaling factor is a number between 0 and 1 which has been determined by the user which is not efficient. We can use the brute force method which solves a problem by checking all the possible cases, but it is slow. In this work, we use simulated annealing heuristic search algorithm to find out the best scaling factor to reach more robust, transparent, high data capacity and resistant watermarking method in medical images. Experimental results show that simulated annealing-based scaling factor determination in frequency domain watermarking gives more robust, resistant, and transparent watermarked images.
4 citations
References
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TL;DR: It is proved that in all permutationonly image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosenplaintext attack, which significantly outperforms the state-of-theart cryptanalytic methods.
Abstract: Permutation is a commonly used primitive in multimedia (image/video) encryption schemes, and many permutation-only algorithms have been proposed in recent years for the protection of multimedia data. In permutation-only image ciphers, the entries of the image matrix are scrambled using a permutation mapping matrix which is built by a pseudo-random number generator. The literature on the cryptanalysis of image ciphers indicates that the permutation-only image ciphers are insecure against ciphertext-only attacks and/or known/chosen-plaintext attacks. However, the previous studies have not been able to ensure the correct retrieval of the complete plaintext elements. In this paper, we revisited the previous works on cryptanalysis of permutation-only image encryption schemes and made the cryptanalysis work on chosen-plaintext attacks complete and more efficient. We proved that in all permutation-only image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosen-plaintext attack. To the best of our knowledge, for the first time, this paper gives a chosen-plaintext attack that completely determines the correct plaintext elements using a deterministic method. When the plain-images are of size ${M}\times {N}$ and with ${L}$ different color intensities, the number ${n}$ of required chosen plain-images to break the permutation-only image encryption algorithm is ${n}=\lceil \log _{L}$ ( MN ) $\rceil $ . The complexity of the proposed attack is $O$ ( $n\,\cdot \, {M N}$ ) which indicates its feasibility in a polynomial amount of computation time. To validate the performance of the proposed chosen-plaintext attack, numerous experiments were performed on two recently proposed permutation-only image/video ciphers. Both theoretical and experimental results showed that the proposed attack outperforms the state-of-the-art cryptanalytic methods.
169 citations
TL;DR: Experimental results demonstrate that the suggested watermarking technique archives high robustness against attacks in comparison to the other scheme for medical images, and verification its robustness for various attacks while maintaining imperceptibility, security and compression ratio.
160 citations
"Compression-Then-Encryption-Based S..." refers background or methods in this paper
...PSNR and SSIM are the measures of imperceptibility offered by any hiding technique.(6) However, BER and NC determine the robustness provided by the data Table 1....
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...From the survey, it is established that a combination of watermarking with a suitable encryption scheme is more popular and having great potential.(2,3,6) Encryption converts EPR data into an unreadable code for an unauthorized person to provide significant authentication of the digital form of medical data....
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...Through making use of watermarking approaches with an appropriate compressionthen-encryption (CTE) scheme helps to provide secure and confidential transmission of sensitive data over bandwidth-constrained networks/IoT environments.(3,6,8) In this article, we develop a CTE based dual watermarking to protect the EPR data for the healthcare system....
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...hiding technique.(6) NPCR and UACI estimate the strength of the encryption scheme....
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TL;DR: The proposed robust and secure DWT, DCT and SVD based multiple watermarking techniques for protecting digital contents over unsecure social networks may find potential solutions in prevention of personal identity theft and unauthorized multimedia content sharing on online social networks/open channel.
122 citations
TL;DR: General concepts of watermarking, major characteristics, recent applications, concepts of embedding and recovery process of watermarks, and the summary of various techniques are highlighted in brief.
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.
78 citations
TL;DR: Experimental evaluation shows that using combination of NSCT, RDWT, SVD and chaotic encryption makes the approach robust, imperceptible, secure and suitable for medical applications.
Abstract: In this paper, a chaotic based secure medical image watermarking approach is proposed. The method is using non sub-sampled contourlet transform (NSCT), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) to provide significant improvement in imperceptibility and robustness. Further, security of the approach is ensured by applying 2-D logistic map based chaotic encryption on watermarked medical image. In our approach, the cover image is initially divided into sub-images and NSCT is applied on the sub-image having maximum entropy. Subsequently, RDWT is applied to NSCT image and the singular vector of the RDWT coefficient is calculated. Similar procedure is followed for both watermark images. The singular value of both watermarks is embedded into the singular matrix of the cover. Experimental evaluation shows when the approach is subjected to attacks, using combination of NSCT, RDWT, SVD and chaotic encryption it makes the approach robust, imperceptible, secure and suitable for medical applications.
76 citations