INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
ECG Steganography and Hash Function Based Privacy Protection of Patients Medical Information S.Neela1,
Dr. V.R.Vijaykumar, B.E.,M.E.,Ph.D2.,
PG Scholar M.E., Applied electronics, Department of ECE, Anna University Regional Centre, Coimbatore – 641 047.
Associate Professor and Head, Department of ECE, Anna University Regional Centre, Coimbatore – 641 047.
Abstract— Data hiding can hide sensitive information into signals for covert communication. Most data hiding techniques will distort the signal in order to insert additional messages. The distortion is often small; the irreversibility is not admissible to some sensitive techniques. Most of the applications, lossless data hiding is desired to extract the embedded data and the original host signal. The project proposes the enhancement of protection system for secret data communication through encrypted data concealment in ECG signals of the patient. The proposed encryption technique used to encrypt the confidential data into unreadable form and not only enhances the safety of secret carrier information by making the information inaccessible to any intruder having a random method. For that we use twelve square ciphering techniques. The technique is used make the communication between the sender and the receiver to be authenticated is hash function. To evaluate the effectiveness of ECG wave at the proposed technique, distortion measurement techniques of two are used, the percentage residue difference (PWD) and wavelets weighted PRD. Proposed technique provides high security protection for patient data with low distortion is proven in this proposed system. Keywords- ECG Steganography, Fingerprint, Lifting Wavelet Transform, Medical data, Privacy Protection, Twelve square ciphers. This technique allows ECG to put out of sight the patient confidential data and thus guarantees the patient’s privacy and confidentiality. The aim is to show that both the Host ECG and stego ECG signals can be used for diagnoses and the difference would be undetectable.
1. INTRODUCTION Nowadays, patients confidential information sent through the public network should be secured and protect. Tolerant protection is essential that a patient can control who will use his/her confidential health details, like the name, and the address them, telephone number, and Medicare number and who can access patient’s data. The primary goal is to provide integrity, confidentiality and accessibility. Steganography is a branch of cryptography that involves hiding information. to reduces the chance of a message being detected. The main aim is to hide patient's confidential data and other physiological information in ECG signal. ECG signal is used because the size of ECG is large compared to other medical images. Therefore, patients ECG signal and other physiological readings such as temperature level, range of blood pressure, and the glucose reading etc., are collected at homes by using Body Sensor Networks will be transmitted and diagnosed by re-mote patient monitoring device.in the same cost that the patient confidentiality is protected against intruders while data traverse in open network and stored in hospital servers.
The work of this project is motivated by investigations from the above and similar research findings. Our main theme is to save patient confidential information from harm by using steganography strategy. From the proposed model, new steganography technique using ECG is formulated and introduces their particular calculations, which are quick and adaptable, but on the other hand are capable of providing high-quality and consistent performance. Information Security is to prevent the unauthorized access, misuse of data, content modification, or denial of access, certainties, and so forth. The essential objective is to give secrecy, trustworthiness and accessibility. The great security in reality is compiling of these arrangements. The strong physical security key is to guard substantial assets like papers, records. Computer security is important to control access over others computer systems, and
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 Network security is vital to secure the local area network. Together, these concepts provide Information security.
Step 2: Row wise processing is evaluated to get LL, LH, HL and HH, Separate even and odd rows of H and L, Namely, Hodd – odd row of H, Lodd- odd row of L, Heven- even row of H Leven – even row of L LH = Lodd-Leven; LL = Leven + (LH / 2) HH = Hodd – Heven; HL = Heven + (HH /2)
Among these, an important sub discipline of hiding information is steganography. Information hiding is recent techniques have become important in a number of applications. It is critical that correspondence must be secured by encrypting the secret messages. Usually means hiding information in other information.
Reverse Lifting scheme
The main target of steganography is to put out of sight the secret message in the other cover media so that nonentity can see that and both participants are converse in secret way. By combining the techniques of steganography and the other techniques, information security has made strides. Steganography is utilized as copyright, guarantee data in a secured manner. Such carriers are the text, also the document, video, image, audio etc., Hiding a message reduces the chance of finding a message. Then the message is also encrypted to provide a layer of protection.
Inverse Integer wavelet transform is formed by Reverse lifting system. For the forward lifting plan additionally the procedure is same.
2.2) Twelve Square Cipher The twelve-square cipher encrypts digits, alphabets and special characters and thus is less susceptible for analyzing frequency related attacks. It uses six 5 by 5 matrices which can be arranged in a square manner. Each of the 5 by 5 matrices contains the letters of the alphabet (usually omitting "Q" to reduce the alphabet to fit into the square) and another six 6 by 7 matrices arranged in squares for digits and special elements. All the special characters and digits from your desktop/laptop keyboard are included in this table.
2. PROPOSED METHODOLOGY 2.1) Lifting Wavelet Transform The wavelet transform has gained widespread acceptance in signal processing in general and in image compression research techniques. The image compression, discrete wavelets transform (DWT) based schemes have outperformed other coding schemes applications like the ones based on DCT. Since there is no need to divide the input image into non-overlapping 2-D blocks and its basis functions have variable width, the wavelet-coding schemes at higher compression ratios avoid blocking artifacts. Because of their inherent multi -resolution nature process and the waveletcoding schemes are especially suitable for applications where scalability and tolerable corruption are needed. Nowadays the JPEG council has discharged its new image coding technique, JPEG-2000, that is based upon DWT.
In Square-1, we have twenty five alphabets the letters q, all the row is modified with five letters. Excluding Square-2 is created with the help of square-1 by taking the first row of square-1 to fifth row place and other rows one position expanded by one stage. square(3) is made from square-2 by taking the first row of square-2 to fifth row place and other rows one position high. Similarly In square-4, we have used a word gmrit in the first row which comprises of the five alphabets and the remaining twenty alphabets are arranged in other four rows continuously excluding the alphabets of the word "gmrit". Square-5 is produced using square-4 by taking the first row to third row. Same as the square-6 is made from square-4 by taking the first row to fifth row location. In square-7, the numerals and special characters from a standard laptop are arranged in six rows and 7 columns are represented. Square-8 is made from square-7 by taking the first row to 6th line place. Like that square-9 is made from square-8 by taking the first row of square-8 to sixth row position. Like that Square-10 is created from square-7 by arranging the row properties in sections. Essentially Square-11 is made from square-10 by taking the first row of square-10 to third line position. The square-12 is developed from square-10 by taking the first row into sixth row place.
Forward transform Step1: Column wise processing to get High frequency and Low frequency components
Where Ce and Co is the even column and odd column wise pixel values
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 to Second table. The first special character, its plain text is in square-7 and cipher text is in same row and column location of square-10. For second special elements, the plain text is in square-8 and cipher text is in same row and column location of square-11. For the third uncommon character the plain text is in square-9 and cipher text is in same row and column location of square-12. like that fourth special character (including numbers) corresponds to square-7 and square-10, 5th special character (including numbers) corresponds to square-8 and square-11,and the 6th special character(including numbers) corresponds to square-9 and square-12 and so on.
Table 1. Plain text and cipher text
2.3) Adaptive LSB Replacement In this approach variable number of LSBs would be utilized for embedding secret message bits according to the mentioned algorithm: For all components of each and every pixel of color image across smooth areas Every pixel value in this image is analyzed and the following checking process is employed for all the three bytes respectively If the value of the pixel say vi, is in the range 240 ≤ vi ≤255, then we embed 4 bits of secret data into the 4 LSBs of the pixel value.
Table 2. Plain text and cipher text
It can be done by detecting the first 3 Most Significant Bits (MSB’s). If they are all 1’s then the remaining 4 LSB’s can be used for embedding message. If the value of vi (First 3 MSB’s are all 1’s), is in the range 224 ≤ vi ≤239 then we embed 3 bits of secret data into the 3 LSB’s of the pixel. If the value of VI is in the range193 ≤ VI ≤223 then we embed 2 bits of secret data into the 2 LSB’s. Other cases for the values in the range 0 ≤vi ≤192 we embed 1 bit of secret data into 1 LSB of the pixel value. Same procedure is adapted for extracting the hidden text from the image.
Digits and special characters The plain text is read from left to right manner. Character is an alphabet it refers to table (1), or if it is a number or a special character it refers to table number two. At the point when examining the plain text the first alphabet’s plain text is in square-1 and its cipher is in same row and column location of square-4. The next alphabet, its plain text is in square-2 and cipher text is in same row and column area of square-5. The 3rd letter set, its plain content is in square-3 and cipher text is in same row and column area of square-6. Like that the fourth letter set relates to square-1 and square-4, 5th letters in order relates to square-2 and square-5, 6th letters corresponds to square-3 and square-6 and so on. The secret message is combination of letters, the numbers and special elements. When scanning the secret data, the uncommon characters and digits it refers
Fig 1 Block Diagram of Proposed Embedding System
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 Most existing adaptive approaches usually assume that the adaptive LSB of natural covers is not sufficient and sufficiently arbitrary, and accordingly those pixels blend for information hiding can be selected freely using a PRNG. The assumption is not always correct, it always true for images with many smooth regions.
segmentation algorithm needs to separate foreground from noisy background which includes all ridge-valley regions. Image enhancement algorithm needs to keep the original ridge flow pattern without altering the broken and join ridges, artifacts between pseudo-parallel and not present false data. At last details identification calculation needs to locate efficiently and accurately the minutiae points. The main difficulty in producing hash functions for fingerprint minutiae is the inability to somehow normalize fingerprint data by finding specific orientation of fingerprint. If data of the fingerprint is not finding, then the values of any hashing functions are destined to be orientation/position dependent. The way to overcome this difficulty is to have hash functions as well as matching algorithm deal with transformations of fingerprint data.
Fig 2 Block Diagram of Extraction of Proposed System It can be clearly observed that the adaptive LSB can reflect the texture information of the cover picture. Reference on extensive experiments, we find that uncompressed natural images usually contain some flat regions and the adaptive LSB in those regions have the same values.
3. SIMULATION RESULTS The performance of used methodology will be evaluated as following.. Here the metrics such as Mean square Error, PSNR, Percentage Residual Difference, Weighted wavelet Percentage Residual Difference and Correlation Coefficient are measured.
If we embed the secret message into these locations, the ALSB of steno images would become more random, it may lead to statistical and visual differences between cover and steno images in the adaptive LSB plane Compared with smooth part, the adaptive LSB of pixels located in edge part usually present more random characteristics, and they are almost similar to the distribution of the secret message bits. Therefore, small amount of detectable artifacts and visual artifacts would be left in the edge part after data hiding. Furthermore, the edge information is highly dependent on image portion, it generate detection even more difficult. So that only the proposed method will initially embed the secret bits into edge regions as far as possible while holding other smooth regions as they are.
The execution of the procedure will be assessed as following,
2.4) Fingerprint and Hash Function matching Minutiae Extraction An accurate representation of the fingerprint image is critical to automatic fingerprint identification device, most of the deployed commercial large-scale systems are dependent on feature-based. From all the fingerprint extractions, and the minutia point features with corresponding orientation maps are unique enough to discriminate amongst fingerprints robustly; the minutiae feature representation reduces the complex fingerprint recognition problem to a point pattern matching problem. In order to achieve high-accuracy minutiae with varied quality fingerprint pictures,
Fig 3 Input Signal
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
Fig 4 Wavelet Decomposition of Input Signal
Fig 7 Watermarked Signal
Fig 5 Binary and Thinned Image
Fig 8 Input texts and its Ciphering text
Table 3 Performance Parameters Percentage Residual Difference (%) Weighted wavelet Percentage Residual Difference (%) Root Mean Square Error Peak Signal to Noise ratio Correlation Coefficient
0.2417 0.0924 4.9992 62.4778 0.9997
4. CONCLUSION In this paper a novel steganography algorithm is proposed to hide patient information as well as in ECG signal. This technique will provide a secured communication and confidentiality in a Point-of-Care system. Multilevel wavelet decomposition is applied to the signal. To encrypt the patient’s data twelve square ciphers is used. After that encrypted data is embedded
Fig 6 Minutiae Points
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 into ECG signal using LSB substitution. While concealing the information unique finger impression and hash function is used in order to secure the data more efficiently.
schemas and documents for healthcare,‖inBioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on, 2012, pp. 782–789.
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