SCHS: Secured Cloud-assisted e-Healthcare System

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 01 | June 2016 ISSN (online): 2349-6010

SCHS: Secured Cloud-Assisted e-Healthcare System Miss. Pooja Kolte PG Student Department of Computer Engineering RMD Sinhgad School of Engineering, Savitribai Phule Pune University, Pune, India

Ms. Kanchan Varpe Assistant Professor Department of Computer Engineering RMD Sinhgad School of Engineering, Savitribai Phule Pune University, Pune, India

Abstract Clinical documents are increasing daily in higher numbers with the patient’s health condition which is recovering and deteriorating continuously. Different existing healthcare systems are used in hospitals to store this health records. Privacy of patients becomes an issue due to these not so secure healthcare systems. This collected data is too large to store on small devices or to compute on it. Cloud-assisted e-healthcare system is proposed to store this huge dynamic data and providing privacy to the patient’s sensitive health records. The problem is that cloud might be semi-honest means it will help to store patient’s private data securely but also curious to know that private data. Therefore here we are proposing Secured Cloud-assisted e-Healthcare System (SCHS). E-healthcare system provides patients health monitoring, disease modeling and evidence based solutions from recognized healthcare providers or physicians. Secure data aggregation technique is used to aggregate data from different sources. For this, a secure and efficient Additive ElGamal algorithm that supports both homomorphic addition and multiplication is used. Finally, performance evaluations demonstrate our system achieves higher security and optimized storage and advantage over communication and computation overhead.

Keywords: E-healthcare system, personal health records, privacy preservation, security, text mining, image feature extraction _______________________________________________________________________________________________________ I.

INTRODUCTION

Hospital information systems [22, 23, 24] and different applications are used in different clinics, hospitals to store clinical documents, Personal Health records (PHR), endoscopy, electrocardiogram (ECG), electroencephalogram (ECR) reports. Some patients need to go for everyday checkup also by covering a long distance. These clinical records [3] increasing daily in huge numbers and the privacy and security of patient’s information may not be maintained by such systems. Private health information of patients should not be revealed by unauthorized users it may cause harm to patient’s health and also effect on his/her Employment, Life Insurance Policy, Claim management solution, etc. e-Healthcare system provides facility for maximum utilization of information for other purposes too like survey of disease by government, case study for medical student and different audits etc. E-healthcare systems designed so data can be accessed from anywhere and anytime.

The main focus in our system is privacy preservation of patient’s private data which is in text and image format too. The data is collected frequently from patient’s devices and it will be processed by that devices means pre-computations are done on PHR. Owing to the patient’s device is resource restricted, and frequent collection from huge number of patients leads to energy consuming task and cannot be afforded by both the patient and the physician. So this private data need to be outsourced onto cloud. We cannot fully trust on cloud because they can try to extract individual information. Encryption is done over data to protect private data from other unauthorized users and from cloud provider too. Encryption used should be lightweight to minimize the computational and communication cost. Secondly, cloud works as “pay per use” model, so it’s very convincing facility for the patients to offload both their storage and the computation.

Patients upload their PHR data to e-healthcare system are in different formats and might be correlated. It needs to aggregate [12] these data in single one and keep it secure on and from medical cloud provider itself. We are providing secure data aggregation algorithm [1]. For security of records we encrypt data before outsourcing it to cloud and then operations are performed on that ciphertext. The homomorphic encryption [5] techniques are used to perform the operation on ciphertext and which provide result exactly like plaintext. The partial homomorphic encryption gives capability to perform either addition or multiplication but not the both. Therefore Additive ElGamal algorithm is proposed scheme which perform both addition and multiplication operation.

II. RELATED WORK In this section we will see brief review of existing work [6, 7, 8, 9, 17, 18, 21] done in healthcare environment. Recently, Jung proposed a privacy preserving data aggregation of different formats which supports multivariate polynomial evaluation technique without secure communication channel [12], in one aggregator model and participant only model. Its implementation supports

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computation for only static data, without including the patient’s dynamic health records which can more precisely reflect his dynamic health condition status untouched.

Homomorphic encryption [5] cryptosystem provides potential solutions in this problem but do not support for more than one computation on ciphertext. ElGamal cryptosystem [16] provided privacy preserving homomorphic encryption solution for computations but its only multiplicative means it does not support homomorphic addition.

Then Paillier also [6] proposed an homomorphic encryption but it also supports only addition and not the multiplication. Hsu et al. proposed an image feature extraction with privacy-preserving in encrypted format scale-invariant feature transform (SIFT) [13], by using paillier’s cryptosystem [6] but it’s unable to apply transform on outsourced medical image feature extraction because paillier’s cryptosystem supports only addition homomorphism. Additionally, the feature descriptor matching in the encrypted domain did not support privacy-preserving inner product, which resulted in the medical image privacy exposure [4].

Rivest, Shamir and Adleman [18] published their public key cryptosystem in 1978 but its homomorphic encryption is multiplicative. Goldwasser-Micali [17] is also a homomorphic cryptosystem used for computation but it is also a partial homomorphic encryption that supports addition but it can encrypt only a single bit.

Boneh-Goh-Nissim [21] proposed technique which supports unlimited number of additions but only one multiplication. Many existing systems are available to solve this encryption problem with some anonymous identity, generating dynamic keys, different energy efficient algorithms. For security and providing both computations on homomorphic encryption we need fully homomorphic encryption (FHE) [1], [2], [20] only.

Therefore in this paper we have proposed a secure privacy-preserving dynamic medical text mining and image feature extraction scheme in e-healthcare system. It supports both addition and multiplication aggregation with a unified mechanism from number of individual data on the ciphertext, requiring to perform one-way trapdoor computation function only once.

Compared to recently proposed available fully homomorphic encryption[14] systems, we are going to use the additive ElGamal cryptosystem [16] which achieves higher security in the semi-honest cloud model and significantly decreases computational and communication cost, ElGamal also optimizes storage required for code which results in fast execution of our system.

III. PROPOSED SYSTEM This proposed e-Healthcare system provides datasets of patients which are real-time personal health records and that are in terms of both text and image (i.e. electrocardiogram (ECG), and endoscopy) also, which is further aggregated and outsourced to the healthcare cloud service provider for the authorized physicians to access and decide corresponding treatment. In smart ehealthcare systems, collected PHR is required to match the medical templates provided by physicians based on past history in the cloud, to judge the state of the patient suffering/recovering from certain diseases.

Secured cloud-assisted e-healthcare System is privacy preserving healthcare system which does dynamic medical text mining and image feature extraction.

Fig. 1: E-Healthcare System Architecture

E-healthcare system is based on fully homomorphic data aggregation which simultaneously supports addition and multiplication [15] with unified mechanism from every individual data in encrypted domain, requiring any one-way trapdoor function computation only once. It significantly reduces the computational and communication cost, that supports privacypreserving inner product in computing the similarity in the encrypted domain.

This proposed system mainly has three components as shown in fig. 1: the patient, the physicians, and the cloud. Patient provides dynamic health condition information i.e. PHR to cloud. These devices are not able to store much data and to compute it. So we need to outsource that data onto the cloud. This PHR is encrypted and send it to the cloud. For encryption ElGamal homomorphic encryption cryptosystem is used and data is aggregated before outsourcing. Physician also upload encrypted template on to the cloud. Cloud server analyses the PHR and template and check the similarity and differences in between that and alerts to patient about his fitness and provide analysed reports to physician for further prescription.

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IV. IMPLEMENTATION STRATEGY Module Description: The Proposed e-healthcare system is based on Additive ElGamal cryptosystem [19] and data aggregation scheme [1]. System generates a single trapdoor function to minimize communication overhead. Our proposed system is going to be implemented in modules as follows.

1) Module 1: UI & Framework Building 2) Module 2: PHR Aggregation & Homomorphic Encryption 3) Module 3: Physician/ Healthcare Provider Sample Template 4) Module 4: Text Mining and Image Feature Extraction 5) Module 5: Analysis & Testing Now, the detailed description of modules in our proposed system is given below: UI & Framework Building: In this module, basic application framework which will take input from web services and login for multiple entities which will give view for operations on data will be created.

Personal Health Record (PHR) Aggregation & Homomorphic Encryption: In this module, personal health records (Images & Text) is collected by web service from patients and processed by web service.

These images and text must be aggregated with help of our aggregation algorithm and encrypted by homomorphic encryption before uploading them on cloud.

The fully homomorphic cryptosystem does encryption without optimization which leads to unwanted increase in ciphertext and key sizes and we will minimize this using Additive ElGamal Algorithm.

Physician’s Sample Template: In this module, physicians collect the disease samples from their past history and encrypt them before upload to cloud.

Text Mining and Image Feature Extraction: In this module, image feature extraction is done in encrypted domain which is different from traditional image feature extraction on plaintext. So privacy preserving scale-invariant feature extraction (SIFT) [13] scheme is used.

Disease modelling will be performed by CSP over encrypted content by comparing PHR and sample template with the help of both the dynamic medical text and medical images.

Analysis & Testing: This is final module and we will perform testing on our application and with help of generated results we will analyse the difference between proposed and existing system.

Secure Data Aggregation: The proposed system uses secure and efficient data aggregation strategy to reduce data redundancy and transmission overhead.

Then different data gathered from patients at different time instances are correlated and then that data is aggregated with the help of related functions into a single high-efficiency one and then outsource it to healthcare cloud.

The proposed scheme is composed of the following four data aggregation algorithms, namely AGG.KGen, AGG.Enc, AGG.Eval and AGG.Dec, which is defined as follows.

1) Step 1: AGG.KGen In this step system is initialized and the keys required for encryption are generated.

PPR = System_auto(device_id, user_id); (PK, SK) = System_auto(random(), user_id); 2) Step 2: AGG.Enc In this step Additive ElGamal is performed taking keys from step 1.

This is the probabilistically polynomial-time encryption algorithm run by the patient. Takes as input a message m, the public key PK and the public parameter PPR, outputs a ciphertext C. Additive ElGamal is completely described below.

3) Step 3: AGG.Eval In this step, the ciphertext generated from step 2 is converted using evaluation function. The evaluator performs the multiplication and addition aggregation operations. This is the evaluation algorithm run by the cloud that takes as input C, a function F and the public parameter.

PPR, outputs the ciphertext CEval.

CEval = Function(C, PPR); 4) Step 4: AGG.Dec This is performed by web service and physician to compare the physician’s sample with the PHI. This is a deterministic and polynomial-time decryption algorithm run by the physician takes as input the ciphertext CEval and the secret key.

Additive ElGamal Encryption: In cryptography, the ElGamal encryption system is an asymmetric key encryption algorithm for public-key cryptography. In this encryption algorithm we call respective aggregation algorithms explained in section B. ElGamal is secure and optimally efficient

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in the sense of memory requirement. Therefore proposed scheme is superior in execution time and communication cost than some existing approach.

ElGamal can be made additive by encrypting g m instead of m with traditional ElGamal for some generator g. This variant is sometimes called exponential ElGamal. The difficulty is in decryption as running the standard decryption gives you g m and recovering m requires you to solve the discrete log. As long as m is small, this can be done algorithmically.

Normal ElGamal scheme is multiplicatively homomorphic: E(x) E(y) = E (xy); If anyone want to make it additively homomorphic [19], we fix some generator g; then we transform the integer x to the group element gx before encrypting with ElGamal. With this transformation; E (gx) E(gy) = E(gxgy) = E(gx+y), Now we have an additive homomorphic property. Now we will see an Additive ElGamal Encryption algorithm in details.

Algorithm: Additive ElGamal Encryption Output: public key kpub and private key kpr 1) function KEYGEN 2) Choose a large prime p 3) Choose a primitive element α Є Z*p 4) Choose an integer a Є {0, . . . , p – 2} 5) β = αa 6) return kpub = (p, α, β), kpr = a 7) end function Input: public key kpub = (p, α, β) and message m Output: ciphertext c 1) function ENCRYPT(m) 2) Choose k Є {2, . . . , p – 2} 3) x = α k mod p

Fig. 2: (a) Memory size comparison of proposed system

Fig. 2: (b) Execution time comparison of proposed system

4) y = α m. βk mod p

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5) return c = (x, y) 6) end function Input: private key kpr = a and ciphertext c = (x, y) Output: message m 1) function DECRYPT(c) 2) m* = x-a. y mod p 3) Recover m from m* = α m 4) return m 5) end function This algorithm helps to minimize computation and communication cost and helps to maintain privacy of patient’s data and physician’s template.

V. EXPERIMENTAL RESULTS AND ANALYSIS The proposed system uses additive ElGamal algorithm which is secure and faster than existing system. This implementation offers fast multiplication while featuring smaller code size and memory usage. As shown in fig.2 (a) we can see memory size required for our proposed solution is less than traditional. Therefore proposed healthcare system helps to reduce the communication and computation cost as compared to existing systems.

Fig.2 (b) demonstrates the execution time comparison of our efficient and secure SCHS system with FHE [5] system. It is observed that as the number of records increases execution time required for our system is very negligible than FHE [5].

VI. CONCLUSION Considering a practical problem of privacy preserving in modern healthcare environment we have presented a privacy preserving protocol that provides dynamic text mining and image feature extraction from secure data aggregation in healthcare clouds. We have used secure Additive ElGamal algorithm for encryption and secure data aggregation model. We have considered this scheme because it optimizes code size of our implementation and also decreases the length of key. ElGamal require low decryption time than existing homomorphic encryption system. Therefore we can say our scheme is more efficient in terms of execution time, storage, communication and computation overhead than the existing healthcare system whose execution time increases with increase in patient’s records.

Federated clouds are getting popular nowadays, but our scheme cannot be directly applied. So it is also a future scope to apply this scheme in federated cloud environment.

ACKNOWLEDGMENT I wish to express my sincere gratitude to the administration of Department of Computer Engineering of RMDSSOE, Pune, India and SPPU University for providing the academic environment to pursue research activities. In particular I would like to thank my guide Ms. Kanchan Varpe, for her dynamic and valuable guidance.

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