A secure steganography for privacy protection in healthcare system

Liu, Jing; Tang, Guangming; Sun, Yi

doi:10.1007/s10916-012-9918-z

Cited by 13 publications

(15 citation statements)

References 22 publications

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“…Many researchers have successfully designed data-hiding methods for digital media such as images, audios, and videos. However, only a few authors have focused on embedding secret messages in ECG signals or healthcare systems [12][13][14]. Data-hiding methods, i.e., ECG steganography approaches, have been applied to ECG signals for securing patient information [15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Overview Of Relevant Workmentioning

confidence: 99%

“…To preserve the originality of the host ECG signal, the use of reversible ECG steganography based on wavelet transform has been suggested [32,33]. Liu et al [14] propose a steganographic method that can provide private data in the medical system with very secure protection. They used the adaptive pixel pair match (APPM) method to embed digits in the pixel value differences (PVD) of the three pixels and the base of embedded digits is dependent on the differences among the three pixels.…”

Section: Overview Of Relevant Workmentioning

confidence: 99%

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An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

2020

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Evolving digital technologies in remote health monitoring require an energy-efficient method for secure and reliable transmission of patient's/user's confidential information from the sensor nodes to the cloud/medical server. Thus, a united scheme of the physiological signal steganography and its communication by benefitting from the unequal significance between different parts of the physiological data are emphasized. We believe higher steganography coding strength and more robust source-channel coding would protect extremely vital parts of the physiological data. Therefore, data integrity and transmission efficiency of packet information achieved in a resilient way. We formulate our idea of joint steganography-source-channel coding (JS 2 C 2) as an optimization problem to simultaneously securing and minimizing the transmission energy consumption. A low-complexity deep learning-based ECG classification algorithm along with its secure and energy-efficient neural JS 2 C 2 transmission for real-time monitoring has been realized. The optimal parameters for our united framework have been calculated by JS 2 C 2 optimization method. Our steganography algorithm unequal steganography embedding (USE) achieves very low wavelet-based weighted percent root-mean-squared difference lower than 0.5%. Furthermore, the high correlation between cover and stego and low end-to-end mean-square error (MSE) indicates resilient imperceptibility and maintains the diagnosability of the physiological signal. Moreover, low MSE between embedded and extracted data validates that embedded confidential data has been extracted with negligible distortion. In addition, for the given distortion, the USE-based framework's energy consumption is much smaller (by 55% in typical application scenario) as compared with the equal steganography embedding-based approach's energy consumption. Keywords Electrocardiography (ECG) • Unequal error protection (UEP) • Unequal steganography embedding (USE) • Joint steganography source coding (JS 2) • Joint source-channel coding (JSC 2) • Joint steganography-source-channel coding (JS 2 C 2) • Deep learning (DL) • Security • Correlation • End-to-end distortion • Energy efficiency

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Section: Overview Of Relevant Workmentioning

confidence: 99%

Section: Overview Of Relevant Workmentioning

confidence: 99%

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

2020

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“…It is then processed for descrambling so as to substitute the coefficient in its own-place and the pixel sequence is obtained. In this model, we estimate a correlation estimator , which signifies the mean correlation in between the individual row off is obtained by (17) (17) Thus, the patient data transmitted through medical image is obtained by equation (18) and the medical data transmitted is reconstructed by means of the inverse DRT. (18) In addition, to retrieve the accurate message data or the patients' information, diagnosis and doctor details are obtained by applying decryption key which is used in the same for encryption at the transmitter terminal.…”

Section: Information Extractionmentioning

confidence: 99%

An evolutionary computing enriched RS attack resilient medical image steganography model for telemedicine applications

Mansour

Abdelrahim

2018

Multidim Syst Sign Process

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The recent advancement in computing technologies and resulting vision based applications has given rise to a novel practice called telemedicine that requires patient diagnosis images or allied information to recommend or even perform diagnosis practices being located remotely. However, to ensure accurate and optimal telemedicine there is the requirement of seamless or flawless biomedical information about patient. On the contrary, medical data transmitted over insecure channel often remains prone to manipulated or corrupted by attackers. The existing cryptosystems alone are not sufficient to deal with these issues and hence in this paper a highly robust reversible image steganography model has been developed for secret information hiding. Unlike traditional wavelet transform techniques, we incorporated Discrete Ripplet Transformation (DRT) technique for message embedding in the medical cover images. In addition to, ensure seamless communication over insecure channel, a dual cryptosystem model containing proposed steganography scheme and RSA cryptosystem has been developed. One of the key novelties of the proposed research work is the use of adaptive genetic algorithm (AGA) for optimal pixel adjustment process (OPAP) that enriches data hiding capacity as well as imperceptibility features. The performance assessment reveals that the proposed steganography model outperforms other wavelet transformation based approaches in terms of high PSNR, embedding capacity, imperceptibility etc.

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“…[36] 2014 Non-overlapping blocks of 4*4 sizes are obtained after partitioning cover image. Likewise Watermark is also divided into 4 equal blocks, before this division watermark image is encrypted using Arnold Transform.…”

Section: Literature Surveymentioning

confidence: 99%

Steganography techniques: a survey

2017

IJLTET

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A secure steganography for privacy protection in healthcare system

Cited by 13 publications

References 22 publications

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data

An evolutionary computing enriched RS attack resilient medical image steganography model for telemedicine applications

Steganography techniques: a survey

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