Protection Of Electronic Health Records

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Decomposition (SVD) for watermarking the EHR. The EHR is further encrypted .... Given a noise-free m×n monochrome image I and its noisy approximation K.

Protection Of Electronic Health Records Dr Sujatha B K1

Mamtha Mohan1

Professor(TCE Department)

Assistant professor (ECE Department)

Ph no.9448963245 1

Ph no.8050000066

M S Ramaiah Institute of Technology, Bangalore. E-mail: [email protected] Affiliation:Jain University

ABSTRACT— A reliable medical image management must

I. INTRODUCTION

ensure proper safeguarding of the Electronic health records.

This era has led to major technological innovation, internet

Safeguarding the medical information of the patients is a

being the forehand in it. The advent of internet being deployed

major concern in all hospitals. Digital watermarking is a

for many applications has made tremendous progress in wide

technique popularly used to protect the confidentiality of

spectrum of fields like medicine, health records, diagnosis etc.

medical information and maintaining them which enhances

Fast and secure access to patient’s records helps to save lives

patient health awareness. In this paper we propose a blend

with timely treatment in emergency situations. Therefore,

of Discrete Wavelet Transform (DWT) and Singular Value

anywhere anytime accessible online health-care or medical

Decomposition (SVD) for watermarking the EHR. The EHR is

systems play a vital role in daily life. It taps down this

further encrypted using key based encryption method for

advantage of the need for wide scale protected accessibility of

access control. The SVD is applied to the approximate and

health records to provide a safe and efficient way of sharing

vertical coefficients of the wavelet transform. The technique

and accessing the patient’s records. The EHRs(electronic

improves EHR protection and facilitates in accounting for

health records) is a methodical way of maintaining, securing

performance parameters like peak signal to noise ratio

and protecting health information of patients. It facilitates high

(PSNR) and Mean Square Error (MSE).Additionally the

quality patient treatment and awareness. EHR contains case

properties of

like

study, diagnosis, blood reports, medication, reports about

Normalized Cross-correlation(NC) ,wavelet energies and

various other conditions and others necessary undergoing

homogeneity between the pixel pairs of host and the encrypted

treatment. Moving to electronic health records is important to

watermarked EHR are exploited.

the modernization and revamping of our healthcare system,

Gray

Level

Co-occurrence

Matrix

Keywords— Discrete Wavelet Transform, Singular

but it poses great challenges in the areas of security, safety and

Value Decomposition, Watermarking, Peak signal to

privacy of patients records. Computerized medical records are

noise ratio, Mean Square Error, Normalized Cross

prone to potential abuses and threats. In the last few years,

Correlation, Electronic Health Record (EHR), Gray

thousands of human fraternity have faced compromises of

Level Co-occurrence Matrix(GLCM).

their health information due to the advent of security lapses at hospitals, insurance companies and government agencies. Various commercial companies make a living,buying and

selling doctor’s prescribing habits to the pharmaceutical

The public key encryption is employed to encrypt the records

companies.

and decrypted using cipher image. This is mainly used to

Additionally sensitive electronic data, especially when stored

enhance the security. It is an asymmetric cryptographic

by a third party, is vulnerable to blind subpoena or change in

protocol which is mainly based on the public key. The

user agreements. The main question here is how to provide a

watermarked EHR is encrypted using the public key and all

secure way of accessing and sharing of the medical records.

the users are shared a private key. The user trying to access the

Since the internet is prone to snooping by intruders we need to

EHR decrypts it using his private key. The strength lies in it

ensure the security of the medical records. Many solutions

being impossible for a properly generated private key to be

have been provided for electronic health records. The patient’s

determined from its corresponding public key. Thus the public

privacy is assured by access control, which verifies the

key may be published without compromising security,

person’s access permission in order to ensure security.

whereas the private key must not be revealed to anyone not

Encryption is also proposed along with restricting access .But

authorized

if the server only holds the decryption key that would be

signatures. Embedded encrypted patient data is stored in

catastrophic. So we propose a design which we refer to as an

central server of a health care provider and shared over the

optimal watermarking technique as a solution to secure and

internet which allows anywhere and anytime access of health

private storage of patient’s medical records. The optimal

records at ease. The electronic health records are secured

watermarking technique is based on SVD and DWT domain

through authenticated access, verifying the identity of the user

for gray-scale images .This has formed from the performance

and validating their access permissions included for their

parameters namely peak signal to noise ratio (PSNR), Mean

access. The proposed approach produces watermarked

Square Error (MSE). PSNR is the ratio between the maximum

encrypted patient data which protects their copyrights and

possible

of

avoids modifications. This further strengthens the security and

noise that

protects the patient’s records from any sort of compromise and

power

of

a

signal

and

corrupting http://en.wikipedia.org/wiki/Noise

the

power

to

read

messages

or

perform

digital

affects the fidelity of its representation. The high spreading of

threats.

broadband networks and new developments in digital

This Paper is organized as follows: Section 2 discusses about

technology has made ownership protection and authentication

the related work, Section 3 deals with the proposed

of digital data since it makes possible to identify the author of

methodology involving the algorithms for watermarking and

an image by embedding secret information to the host image.

extraction as well as the performance parameters considered.

The Properties of Gray Level Co-occurrence Matrix like

A discussion of the experimental results is done in Section 4.

Normalized Cross-correlation(NC) ,wavelet energies and

Section 5 discusses the conclusion and the future work.

homogeinity between the pixel pairs of host and the encrypted

II. RELATED WORK

watermarked EHR are exploited for enhancement of the EHR security.

There are a number of potential applications under the

The algorithm embeds the watermark by modifying the

Umbrella of privacy-preserving data sharing and processing.

singular values of the host records. It is followed by singular

There has been considerable research at de-identification of

value decomposition and packing of values and encrypting.

medical record information[1] and de-identification of clinical

Encryption schemes with strong security properties will

records[2].Various other attempts on de-identification of visit

guarantee that the patient's privacy is protected (assuming that

records have been done [3],But these do not include the entire

the patient stores the key safely).

medical records.

Giakoumaki et al proposed a wavelet transform-based

yielding better performance. Haar wavelet is a sequence of

watermarking, the drawback is that it is related only to

rescaled "square-shaped" functions which together form

medical images and not the entire records and also medical

a family or basis. The Haar wavelet's mother wavelet

images are overwritten which may be unacceptable in

function

can be described as

diagnosis. We plan to work on digital watermarking, which would help ensuring the privacy and security of digital media and safeguard the copyright, and hiding the ownership identification [4]. Watermarking is a process that embeds data

Its scaling function

can be described as

into a multimedia object to protect the ownership to the objects [5] Encryption is a solution to secure and private storage of patient’s medical records [8]. The hierarchical

2.Singular Value Decomposition (SVD)

encryption system partitions health records into a hierarchical structure, each portion of which is encrypted with a

Singular Value Decomposition is a matrix factorization

corresponding key. The patient is required to store a root

technique. The SVD of a host image is computed to obtain

secret key, from which a tree of sub keys is derived [6].

two orthogonal matrices U, V and a diagonal matrix the

III. METHODOLOGY Watermark embedding

process is performed on the new matrix S+kW to get Uw, Sw and Vw, where k is the scale factor that controls the strength of the watermark embedded to the original image. Then, the

Host Image

watermarked record Fw is obtained by multiplying the

Encrypted watermarked image

Watermarked image(DWT w +SVD)

matrices U, Sw, and VT . The steps of watermark embedding are summarized as follows:

Cover image(EHR)

Encrypted watermarked image

watermark W is added to the matrix S. Then, a new SVD

Recovered vv watermark Decrypted watermarked image

i. The SVD is performed on the original image (F matrix). F = USVT

IDWT+SVD dewatermark

(1)

ii. The watermark (W matrix) is added to the SVs of Reconstructed original image

Watermark extraction

the original image (S matrix). D = S + kW

(2)

iii. The SVD is performed on the D matrix. Fig1: Block diagram of the proposed methodology.

1.Discrete Wavelet Transform

D = UwSwVT

(3)

iv. The watermarked image (Fw matrix) is obtained using the

Wavelet transform has the capacity of multi-resolution

modified SVs (Sw matrix).(EHR)

analysis. Embedding of a watermark is made by modifications

Fw = USwVT

of the transform coefficients using haar wavelet. The inverse

v.Apply the key based encryption to the watermarked EHR.

transform is applied to obtain the original record. The host

The medical images considered are MRI images. Initially the

image is decomposed into four sub-bands namely LL, LH,

watermark is embedded in the image by setting an initial value

HL, and HH. A hybrid DWT-SVD based watermarking

of scaling factor α. Using gray level Co-occurrence matrix the

scheme which is further encrypted using key based encryption

properties like wavelet energies,homogeinity and cross

method is developed that requires less computation effort

correlation of the medical document(pixel pairs) are measured

(4)

The original record is reconstructed from the encrypted EHR by extraction of the watermark. Optimum value of scaling

(6)

factor is found by iteration of the above and tabulating the

Here, MAXI is the maximum possible pixel value of the

results to obtain desirable values of PSNR, MSE, NC,wavelet

image=255.

energies and homogeinity.

MSE is defined as

3.1 Algorithm to embed watermark into cover image (7)

Steps: i. Read the cover image & watermark EHR.

Cross-correlation is a measure of similarity of two waveform

ii.Apply DWT to cover image to obtain approximation,

as a function of a time-lag applied to one of them.

horizontal, vertical, diagonal DWT coefficients(LL, HL, LH,

For continuous functions ‘f’ and ‘g’, the cross-correlation is

HH) Calculate the approximate DWT coefficient by adding

defined as

the watermark record using Cal(i,j)=ca1(i,j)+(α*watermark)

dt

(5)

(8)

iii. Where Ca1 & ca1 are the modified & original

Where f* denotes the complex conjugate of ‘f’ and‘ ’ is the

approximation coefficients and α is a scaling value as set to10.

time lag.

iv. Apply SVD to the decomposed sub-bands(LL) and (HL)

3.Grayscale co-occurrence matrix(GLCM)

and Encrypt the decomposed record.

GLCM is an m x n x p array of valid gray-level co-occurrence

v.Find the Inverse DWT and decrypt the watermarked record.

matrices.graycoprops normalizes the gray-level co-occurrence

vi. Increment α, apply SVD and inverse DWT.

matrix (GLCM) so that the sum of its elements is equal to 1.

3.2 Algorithm for Watermark Extraction

The energy of each sub bands of the EHR is calculated as

i. Extract the watermark from vertical & approximation DWT coefficient as per the equation WN= (SN-S)/ α; where α=10

‐dimensional DWT, to obtain the first level

(9)

ii. Apply two

Homogenity measures the closeness of the distribution of

decomposition of the watermarked image. LL1, HL1, LH1,

elements in the GLCM to the GLCM diagonal.

HH1 iii.Decrypt the watermarked record and calculate the

(10)

performance parameters.

3.4 Key based Encryption

3.3 Performance parameters

The public key encryption is employed to encrypt the records

The peak signal-to-noise ratio (PSNR) is used to measure the

and decrypt using cipher image. This is mainly used to

quality of reconstructed records. PSNR is the ratio between

enhance the security. It is an asymmetric cryptographic

the maximum possible power of a signal and the power of

protocol which is mainly based on the public key. The

corrupting noise that affects the fidelity of its representation.

watermarked EHR is encrypted using the public key and all

Because many signals have a very wide dynamic range, PSNR

the users are shared a private key. The user trying to access the

is usually expressed in terms of the logarithmic decible scale.

EHR decrypts it using his private key. It is asymmetric

PSNR is most easily defined via the mean squared error

cryptography in terms that where a key used by one party to

(MSE). Given a noise-free m×n monochrome image I and its

perform either encryption or decryption is not the same as the

noisy approximation K.

key used by another in the counterpart operation.

The PSNR is defined as

IV.RESULTS

The medical images considered are MRI obtained from the

range of found to give unrealistic PSNR values of 0 or

database “MRI Images”.

infinity. Hence 62 to 80 dB while the MSE is in the range of

Fig 2(a): watermark embedding

Recovered_ watermark

Watermark+de crypted image

Reconstructed

Original

Watermarked

Watermark

Cover image

image

Image

Image

+encrypted

+watermarked

image

image

MRI Image

MRI Image

0.0015 to 0.048. With increasing values of scaling factor. Fig 2(b): watermark extraction

Wavelet energies of the sub bands ranges from 0.4949 to 0.5461 and homogeneity has a decreasing gradient from

Table 1: performance parameters

0.9870 to 0.8147 portrayed in TABLE 2.The watermarked EHR is encrypted using the public key and all the users are

Scaling

PSNR

factor

in dB

MSE

NC

shared a private key as shown in fig 2(a). The user trying to access the EHR decrypts it using his private key. This is portrayed in fig 2(b).

8

80.38

0.00059

0.0213

9

74.984

0.0021

0.0233

V.CONCLUSION AND FUTURE WORK

10

71.08

0.0051

0.0253

The watermarking can be used to provide proof of the

11

67.06

0.0104

0.0264

12

65.07

0.0175

0.0273

right person. The watermark has been inserted without

14

62.825

0.0339

0.0260

interfering with the documents usefulness. In this paper we

authenticity of EHR that is to say that the medical information of one patient has been issued from the right source and to the

have used DWT+SVD techniques to calculate PSNR, MSE, Table 2.subbands parameters

NC, wavelet energies and the homogeneity which are the properties of gray level co-occurrence matrix of different

Subbands

Energy

Homogeneity

LL

0.4949

0.9870

HL

0.5421

0.8628

LH

0.5137

0.8243

paper contributes in utilizing SVD generous properties along

HH

0.5461

0.8147

with hiding, protecting and safeguarding EHR which is an

medical records.SVD is very aptly used with DWT. It has been tested for different images and PSNR, MSE, NC are calculated for each image.Matlab R2013a has been used. This

asset to all the medical fraternity and the most important the The images have been watermarked using EHR and encrypted.

patient’s awareness regarding their medical health. This paper

On experimentation the scaling factors below 8 and above 14

also introduces new trends and challenges in using SVD and

were the scaling factor range is fixed in the range of 8 to 14.

key based encryption in image processing applications. This

From TABLE 1 it can be observed that PSNR values are in the

paper opens many tracks for future work in using SVD as an

enhances

37, Part 6, December 2012, pp. 723–729._c Indian

confidentiality, security and authenticity of medical health

Academy of Science Conference on Artificial Intelligence in

imperative

tool

in

signal

processing

and

Computer Science ,Malaysia (2013): 147-156.

records by encrypting the medical records using public key based encryption that assures confidentiality and authenticity.

[9]

and efficient health data management through multiple

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