Applying Information Security to Healthcare Services

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EPR (Electronic Patient Records) hiding within medical images

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Applying Information Security to Healthcare Services

  1. 1. Applying Information Security to Healthcare Services A Review Study Marwa Yousif The advent of the internet and the widespread use of relatively low cost computing and networking devices, make it possible for innovative ways of medical collaboration and healthcare services provision through e- health and telemedicine programs. A need for an effective healthcare information system, with reliable storage and secure ways to exchange patient information arises. Reliable storage can be achieved by having complete medical information of patients available in one consistent application and not in dispersed information systems; that reduces cost and efforts of handling such information. Also, protection of the Integrity and confidentiality of medical images has become an essential issue in the management of patients’ medical records. Data Hiding in Medical Images Hiding EPR (Electronic Patient Records) data within medical images has been a dynamic research area in recent years, because of its significance in telemedicine and computer- aided diagnosis applications. Steganography and watermarking techniques has been widely used in research and application areas to hide EPR within medical images to reduce storage and information management cost for health care facilities in an e- health environment and to ensure confidentiality and integrity of the hidden data. Both of them aim at hiding data within signals. But whereas steganography aims for imperceptibility to human eye and also high payload, digital watermarking considers the robustness against removal a top priority. Data hiding within images is categorized into reversible and irreversible methods. In the later the distortion caused by data embedding is permanent and the original image can not be retrieved from the stego image; while at the reversible data hiding methods the original image can be retrieved without significant loss of information. Reversible data hiding methods are more suited to medical images, as they are sensitive to the distortion that irreversible methods cause, which
  2. 2. may risk a proper diagnosis and illness treatment in an e- health environment. There are three groups of reversible data hiding: data compression, pixel-value difference expansion (DE) scheme, and histogram-based scheme [1]. Related Work C. C. Chang, Z. H. Wang, and Z. X. Yin, have proposed a data hiding method employs the least significant bits to embed secret data [2]. In essence, each color image is composed of red-green- blue planes; their scheme embeds secret bits into one of the planes of the color image to enhance the security of the transmitted message. The definition of the blood vessels plays a key role in the quality of a retinal image, so they embed data in a Region of Interest (ROI) that exclude the blood vessels and the background of the retinal image. Their method is ingenious as they have described it in the title of their research; they have managed to avoid distorting the pivotal region of the retinal image which is the blood vessels that have got the diagnostic property of the retinal diseases and has a high clinical value, by embedding the EPR in other less important regions of the Fundus image. Also, the method is replicable by other researchers due to its simplicity and clarity. However, embedding such sensitive data as the EPR within the least significant bits of an image introduces security and integrity issues as the embedded data can be easily discovered and removed; least significant bits are the first place that is expected by attackers. Rajesh Barapate, Dr. Suresh Mali, and Dr. Dinesh Yadav have proposed a method that increases the payload of the EPR data within medical images in [3]. It is a data hiding scheme within the low and mid frequencies of the Discrete Cosine Transform “DCT” coefficients of a medical image to allow for a higher degree perceptual quality of the stego- image and avoid distortion that may result due to direct embedding in the spatial domain. Also, the scheme takes care of the essential parts of the image that have the most important diagnostic features (Region of Interest ROI) and embed the EPR in less diagnostically important parts. Capacity (payload) is an imperative parameter for any data hiding scheme especially within medical images, as it involves high volume of Electronic patient records “EPR” data. The scheme addresses capacity by integrating a new coding technique called Class Dependent
  3. 3. Coding Scheme “CDCS”, which combines both variable and fixed length coding to get less number of bits represent the same amount of information. The scheme provide for high capacity of text EPR data, and also acceptable perceptual stego image quality as the PSNR is equal to or greater than 40 DB; however, it does not provide for high capacity of other type of EPR data such as images, which are common in the field of telemedicine and EPR data. Fatma E.-Z. A. Elgamal, Noha A. Hikal, and F.E.Z. Abou-Chadi have proposed a scheme that allows secure medical images sharing over cloud computing environment [4]. Cloud computing have introduced revolutionary business model in terms of cost and simplicity; however it has also introduced challenges such as the integrity and confidentiality of the shared data that are needed to be addressed in any cloud adoption especially in the field of telemedicine where people health and lives are at stake. The scheme implements two approaches in order to provide three security levels for the shared medical image. The first is the authentication between the owner of the data and the destination, the second is the authentication between the owner of the data and the cloud service provider, and the third is the authentication from the destination of the data to its owner. As previously mentioned, two approaches along with encryption were used to implement these security levels; the first applies spatial watermarking technique that embed data within the Least Significant Bits (LSBs) of the image, and the second uses a hybrid of spatial and DCT transform techniques. The strength of this method comes from taking into account the cloud computing model which is most likely the future preferred business model. However, the spatial embedding part of the method utilizes the LSBs which considered the least secure method as it is the first place where an attacker looks. Also, it uses only text data as the EPR in the experiments, while in practice, EPR data may include other data format such as images. We do not know to which extent the impeccability property would degrade if using images for example as EPR data. As much as this research concerns, no experiment regarding the Fundus image, which considered one of the most distortion sensitive medical image. Sukhmeet Kaur Brar, and Gianetan Singh Sekhon have proposed a method that provides for both a higher capacity and a better image perceptual quality [5]. Payload is increased by encoding the
  4. 4. EPR data using Class Dependent Coding scheme CDCS that allows representing the same amount of information in less bits, and also by embedding in the LSBs of the image which allows higher capacity as well. The method achieve less distortion to the host image by embedding in the virtual borders using the modified difference expansion watermarking, in which the size of the watermarked image is increased by two points in height which implies embedding at Regions of Non Interest RONI (within the virtual borders). In spite of the relatively high capacity, and the less distortion that the method brings, there are limitations need to be considered when applying the methods for data hiding within digital images. First, while increasing the capacity due to LSB embedding, the confidentiality and integrity of both hidden data and the host image are compromised that the LSBs are the less secure place for hiding data. Also, the dynamic size of the image is not maintained as it is increased by 2 points in height which also compromises the confidentiality of the embedded EPR. Zhenfei Zhaoa, Hao Luoc, Zhe-Ming Luc, and Jeng-Shyang Pand have proposed a reversible data hiding scheme based on histogram modification [6]. Their work based on modifying the histogram of the differences between neighboring pixels- which are equal to or close to zero- by the secret data. It is a multilevel histogram modification which leads to a higher capacity compared to other one or two levels conventional histogram modification methods. Also, as the differences concentricity around zero is improved, the distortions on the host image introduced by secret content embedding is mitigated. The method addresses capacity in terms of the multilevel histogram modification using more peaks for data embedding; and also addresses imperceptibility and stego image perceptual quality by using the histogram of the differences of neighboring pixels instead of direct embedding in the host image’s histogram; however, the authors have compared the capacity and imperceptibility of their method to other histogram methods only; from a marketing point of view, why should a healthcare service implement their method given all other spatial and transform domain available methods, they did not prove that their method is superior compared to other non- histogram methods in terms of capacity and imperceptibility.
  5. 5. R.F. Olanrewaju, Othman. O. Khalifa, Aisha Hassan Hashim, Akram M. Zeki and A.A. Aburas, have avoided in [7] the possible distortion caused by adding the hidden data directly to the host image, by embedding an EPR watermark within the complex coefficients of the Fast Fourier transform (FFT) of the host medical image (Fundus & Mammograms) using neural network, in a method they entitled: Complex Valued Neural Network (CVNN). The method involves mapping the watermark bits to the synapse weight of the host image instead of direct embedding to avoid distortion or loss of information in the original image. It uses Complex neural networks to avoid loss of information that the embedding is in both the real and imaginary parts of the host image. Although they have proved that their method is distortion- free and also no loss of information caused by embedding EPR data, which is a pivotal diagnostic criterion for medical images specially the retinal ones, they have not addressed the capacity, which is also an imperative parameter in today’s telemedicine environment. Summary of Reviewed Methods Method Description (Main Features) Medical Image Data Hiding Parameters (Capacity, Imperceptibility, Confidentiality) Strengths Limitations An Ingenious Data Hiding Scheme for Color Retinal Image Tries to overcome the challenge of embedding EPR data without distorting the pivotal diagnostic blood vessels within the retinal images. -Avoid the distortion of the diagnostically sensitive blood vessels by embedding in other regions. -Increased capacity due to embedding within the LSBs of the image. The LSB embedding compromises the security (confidentiality and integrity) of the resulted image. EPR Hiding in Medical Images with CDCS and Energy Thresholding -Uses CDCS method to increase capacity. -Embed in DCT in low and mid frequency coefficients. -Capacity: Increased capacity for Text EPR data. -Imperceptibility: The Stego- images gives PSNR value more than 40dB Capacity: Image EPR data are not represented in CDCS coding, so the increased capacity for only text EPR data. Secure Medical Images Sharing over Cloud Computing environment Two security approaches were presented to guarantee a secure sharing of medical images over the cloud computing environment by providing the mean of trust management between the authorized parities of these data and also allows the Security over the Cloud: Takes into account the cloud computing model which is more likely to be the preferred business model in the future -LSB Embedding: considered the least robustness against removal embedding method. -Uses only text data as EPR, We do not know to what extent the
  6. 6. Conclusion It has been recognized that all the investigated methods tradeoffs between the capacity, imperceptibility, and security parameters of a data hiding scheme. While some focus on capacity and compromise security others focus on imperceptibility and compromise the capacity. Future researches should focus on optimizing the three parameters so that we obtain an Image/ EPR scheme that is secure, diagnostically reliable, and large enough to accommodate the ever growing EPR data. privacy sharing of the EPR. impeccability property would degrade if using images for example as EPR data. A Hybrid Watermarking Approach Using Difference of Virtual Border and CDCS for Digital Image Protection -uses a technique named Class Dependent Coding Scheme (CDCS) that allows less number of bits to represent the same information. -embeds using modified difference expansion watermarking using LSB replacement in the virtual border technique which implies embedding in RONI -Increased capacity (CDCS EPR & LSB embedding) -Less distortion as the embedding in RONI -LSB Embedding: while increasing the capacity, the goal of the method, it is compromising confidentiality The dynamic size of the image is not maintained as it is increased by 2 points in height Reversible data hiding based on multilevel histogram modification and sequential recovery -This paper proposes a reversible data hiding scheme based on Histogram modification. -principle is to modify the histogram constructed based on the neighbor pixel differences instead of the host image’s histogram. Higher capacity due to Using multilevel Histogram modification compared to one or two levels histogram modification methods. - the capacity and imperceptibility have been compared to other histogram methods only.) Distortion free embedding in the Optic Disk of Retina Fundus Images Using Complex-Valued Neural Networks -This paper presents a distortion- free method for embedding data in the optic nerves of the retinal images (Fundus) in the Fourier transform of the image. -This is accomplished by Complex- Valued Neural Networks “CVNN” method. No distortion or loss of information caused by embedding EPR data. The main limitation is that it excludes and ignores capacity (Payload).
  7. 7. References: 1- Li-Chin Huang, Lin-Yu Tseng, and Min-Shiang Hwang, “The Study on Data Hiding in Medical Images”, International Journal of Network Security, Vol.14, 2012; 2- C. C. Chang , Z. H. Wang , and Z. X. Yin, “An Ingenious Data Hiding Scheme for Color Retinal Image”, Proceedings of the Second Symposium International Computer Science and Computational Technology (ISCSCT ’09) Huangshan, P. R. China, 26- 28,Dec. 2009; 3- Rajesh Barapate, Dr. Suresh Mali, Dr. Dinesh Yadav, “EPR Hiding in Medical Images with CDCS and Energy Thresholding”, 1st International Conference on Recent Trends in Engineering & Technology, Mar-2012 Special Issue of International Journal of electronics, Communication & Soft Computing Science & Engineering, 2012; 4- Fatma E.-Z. A. Elgamal, Noha A. Hikal, F.E.Z. Abou-Chadi, “Secure Medical Images Sharing over Cloud Computing environment”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 5, 2013; 5- Sukhmeet Kaur Brar, Gianetan Singh Sekhon, “A Hybrid Watermarking Approach Using Difference of Virtual Border and CDCS for Digital Image Protection”, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com Volume 2, Issue 8, August 2012; 6- Zhenfei Zhaoa,b, Hao Luoc, Zhe-Ming Luc, Jeng-Shyang Pand, “Reversible data hiding based on multilevel histogram modification and sequential recovery”, International Journal of Electronics and Communications (AEÜ) journal homepage: www.elsevier.de/a eue, 2011; 7- R.F. Olarnewaju, O.O. Khalifa, Aisha Abdulla, A.A Aburas, and A. MM. Zeki, “Distortion- Free Embedding in the Optic Disc of Retina Fundus Images Using Complex-Valued Neural Networks”, World Applied Science Journal, 2011.

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