Bonfring International Journal of Software Engineering and Soft Computing, Vol. 9, No. 2, April 2019 43 Block Chain Technology for Privacy Protection for Cloudlet-based Medical Data Sharing M. Divya and Dr.G. Singaravel Abstract--- With the popularity of wearable devices, along with the development of clouds and cloudlet technology, there has been increasing need to provide better medical care. The processing block chain of medical data mainly includes data collection, data storage and data sharing, etc. Traditional healthcare system often requires the delivery of medical data to the cloud, which involves users’ sensitive information and causes communication energy consumption. Practically, medical data sharing is a critical and challenging issue. Thus in this paper, we build up a novel healthcare system by utilizing the flexibility of cloudlet. The functions of cloudlet include privacy protection, data sharing and intrusion detection. In the stage of data collection, we first utilize Number Theory Research Unit (NTRU) method to encrypt user’s body data collected by wearable devices. Those data will be transmitted to nearby cloudlet in an energy efficient fashion. Secondly, we present a new trust model to help users to select trustable partners who want to share stored data in the cloudlet. The trust model also helps similar patients to communicate with each other about their diseases. Thirdly, we divide users’ medical data stored in remote cloud of hospital into three parts, and give them proper protection. Finally, in order to protect the healthcare system from malicious attacks, we develop a novel collaborative intrusion detection system (IDS) method based on cloudlet mesh, which can effectively prevent the remote healthcare big data cloud from attacks. Our experiments demonstrate the effectiveness of the proposed scheme. Combines classical partitioning algorithms with probabilistic models so as to form an efficient clustering approach. Keywords--- IDS, Cloudlets Mining, Number Theory, Encrypt, Block Chain. I. INTRODUCTION T HE client’s physiological data are first collected by wearable devices such as smart clothing. Then, those data are delivered to cloudlet. The following two important problems for healthcare data protection is considered. The first problem is healthcare data privacy protection and sharing data. The second problem is to develop effective countermeasures to prevent the healthcare database from being intruded from outside. A. Block Chain Blockchain, the technology behind Bitcoin, seems to M. Divya, PG Scholar, Department of Information Technology, K.S.R. College of Engineering, Tiruchengode, India. Dr.G. Singaravel, Professor & Head, Department of Information Technology, K.S.R. College of Engineering, Tiruchengode, India. DOI:10.9756/BIJSESC.9021 be the driving technology behind the next generation of Internet, also referred to as the Decentralized Web, or the Web3. Blockchain is a novel solution to the age-old human problem of trust. It provides an architecture for so-called trustless trust. It allows us to trust the outputs of the system without trusting any actor within it. A Blockchain protocol operates on top of the Internet, on a P2P Network of computers that all run the protocol and hold an identical copy of the ledger of transactions, enabling P2P value transactions without a middleman though machine consensus. Blockchain itself a file – a shared and public ledger of transactions that records all transactions from the genesis block (first block) until today. Blockchain is a shared, trusted, public ledger of transactions, that everyone can inspect but which no single user controls. It is a distributed database that maintains a continuously growing list of transaction data records, cryptographically secured from tampering and revision. The ledger is built using a linked list, or chain of blocks, where each block contains a certain number of transactions that were validated by the network in a given timespan. The crypto-economic rules sets of the blockchain protocol (consensus layer) regulate the behavioural rules sets and incentive mechanism of all stakeholders in the network. • Blockchain technology is like the internet in that it has a built-in robustness. By storing blocks of information that are identical across its network, the blockchain cannot: • Be controlled by any single entity. • Has no single point of failure. Bitcoin was invented in 2008. Since that time, the Bitcoin blockchain has operated without significant disruption. (To date, any of problems associated with Bitcoin have been due to hacking or mismanagement. In other words, these problems come from bad intention and human error, not flaws in the underlying concepts.) The internet itself has proven to be durable for almost 30 years. It’s a track record that bodes well for blockchain technology as it continues to be developed. B. Content Sharing and Privacy Protection Introduce the encryption process for users’ privacy data, which prevents the leakage or malicious use of users’ private data during transmissions. Next, we present the identity management of users who want to access to the hospital’s healthcare data. Thus, we can assign different users with different levels of permissions for data access, while avoiding data access beyond their permission levels. Finally, we give an application of using users’ private data, which is beneficial to ISSN 2277-5099 | © 2019 Bonfring Bonfring International Journal of Software Engineering and Soft Computing, Vol. 9, No. 2, April 2019 both users and doctors. Based on the healthcare big data stored in the remote cloud, a disease prediction models built based on decision tree. The predictions will be reported to the users and doctors on demand. C. Encryption at the User End When using wearable devices to collect users’ data, the procedure inevitably involves the user’s sensitive information. Therefore, how to effectively collect and transmit users’ data under efficient privacy protection is a critical problem. In a data collection method, called PHDA, is proposed based on data priority which can give proper cost and delay to different priorities data. In, Li et al. discuss the process of data collection and utilizes sum aggregation to obtain data to make sure the security of users’ privacy in the presence of unreliable sensors. In , Lu et al., study 3V data privacy protection issue based on big data of healthcare. Based on the model presented in, this paper utilizes the advantages of NTRU encryption scheme. NTRU can protect the user’s physiological data, such as heart rate, blood pressure and Electrocardiography (ECG), etc. Before transmitted to a smartphone, NTRU encryption scheme executed. The encrypted data will then be stored in the cloudlet through a cellular network or WiFi. We hereby describe the processes of encryption and deciphering in the following. In order to protect medical data, we also develop an intrusion detection system in this paper. Once a malicious attack is detected, the system will fire an alarm. This section presents a novel scheme to build a collaborative IDS system to deter intruders. In the following, we first consider what happens if the system is suffering from different attacks, while detection rates for individual IDS vary with the cloudlet servers. We will plot the detection rate and false alarm rate as the receiver operating characteristic (ROC) curves. Next, we evaluate the collaborative detection rate and estimate the expected cost of implementation in the cloudlet mesh. We apply a decision tree to choose the optimal number of IDS’s to be deployed on the mesh. The goal is to achieve prescribed detection accuracy against the false alarm rate under the premise of minimizing the system cost. Collaborative IDS is designed among m IDS, S1, S2, Sm, in order to get higher detection rate and lower false alarm rate. The IDS assumed to be detect Independently. There exists a K different type of intrusion. So according to deduce in the following, we can get the detection rate and false alarm rate of collaborative IDS. In order to evaluate it , we give the ROC curve. Before transmitting data to the remote cloud, we establish the collaborative IDS based on the cloudlet mesh to 44 complete the intrusion detection task. We use {S1, S2, . . . , Sm} to represent the set of IDS’s in the IDS (CIDS) .Suppose that each IDS is able to detect intrusion independently. For the sake of simplicity, we use I to indicate that there is intrusion behaviour. II. RELATED WORKS M. S. Hossain et al., [1] the potential of cloud-supported cyber-physical systems (CCPSs) has drawn a great deal of interest from academia and industry. CCPSs facilitate the seamless integration of devices in the physical world (e.g., sensors, cameras, microphones, speakers, and GPS devices) with cyberspace. This enables a range of emerging applications or systems such as patient or health monitoring, which require patient locations to be tracked. These systems integrate a large number of physical devices such as sensors with localization technologies (e.g., GPS and wireless local area networks) to generate, sense, analyse, and share huge quantities of medical and user-location data for complex processing. However, there are a number of challenges regarding these systems in terms of the positioning of patients, ubiquitous access, large-scale computation, and communication. Hence, there is a need for an infrastructure or system that can provide scalability and ubiquity in terms of huge real-time data processing and communications in the cyber or cloud space. To this end, this paper proposes a cloudsupported cyber-physical localization system for patient monitoring using smartphones to acquire voice and electroencephalogram signals in a scalable, real-time, and efficient manner. The proposed approach uses Gaussian mixture modelling for localization and is shown to outperform other similar methods in terms of error estimation. K. T. Pickard and M. Swan [2] Sharing personal health information is essential to create next generation healthcare services. To realize preventive and personalized medicine, large numbers of consumers must pool health information to create datasets that can be analysed for wellness and disease trends. Incorporating this information will not only empower consumers, but also enable health systems to improve patient care. To date, consumers have been reluctant to share personal health information for a variety of reasons, but attitudes are shifting. Results from an online survey demonstrate a strong willingness to share health information for research purposes. Building on these results, the authors present a framework to increase health information sharing based on trust, motivation, community, and informed consent. J. Yang, J. Li, J. Mulder, Y. Wang [3] The appropriate collection and consumption of electronic health information about an individual patient or population is the bedrock of modern healthcare, where electronic medical records (EMR) serve as the main carrier. This paper first introduces the main goal of this special issue and gives a brief guideline. Then, the present situation of the adoption of EMRs is reviewed. After that, the emerging information technologies are presented which have a great impact on the healthcare provision. These include health sensing for medical data collection, medical data analysis and utilization for accurate detection and prediction. Next, cloud computing is discussed, as it may ISSN 2277-5099 | © 2019 Bonfring Bonfring International Journal of Software Engineering and Soft Computing, Vol. 9, No. 2, April 2019 provide scalable and cost-effective delivery of healthcare services. Accordingly, the current state of academic research is documented on emerging information technologies for new paradigms of healthcare service. At last, conclusions are made. Y. Wu, M. Su, W. Zheng, K. Hwang [4] In a community cloud, multiple user groups dynamically share a massive number of data blocks. The authors present a new associative data sharing method that uses virtual disks in the MeePo cloud, a research storage cloud built at Tsinghua University. Innovations in the MeePo cloud design include big data metering, associative data sharing, data block prefetching, privileged access control (PAC), and privacy preservation. These features are improved or extended from competing features implemented in DropBox, CloudViews, and MySpace. The reported results support the effectiveness of the MeePo cloud. Kirti A. Dongre ; Roshan Singh Thakur [5] Cloud computing is one of the upcoming technologies that will upgrade generation of Internet. The data stored in the smart phones is increased as more applications are deployed and executed. If the phone is damaged or lost then the information stored in it gets lost. If the cloud storage can be integrated for regular data backup of a mobile user so that the risk of data lost can be minimized. The user can stored data in the server and retrieve them at anytime and from anywhere. The data might be uncovered by attack during the retrieval or transmission of data using wireless cloud storage without proper authentication and protection. So to avoid this in this paper we design a mechanism that provides a security requirement for data storage of mobile phones. X.Xue and W.B.Croft [6] propose a novel framework where the original query is transformed into a distribution of reformulated queries. A reformulated query is generated by applying different operations including adding or replacing query words, detecting phrase structures, and so on. Since the reformulated query that involves a particular choice of words and phrases is explicitly modelled, this framework captures dependencies between those query components. On the other hand, this framework naturally combines query segmentation, query substitution and other possible reformulation operations, where all these operations are considered as methods for generating reformulated queries. In other words, a reformulated query is the output of applying single or multiple reformulation operations. Jimeng Sun, Fei Wang, Jianying Hu, Shahram Edabollahi[7] Patient similarity assessment is an important task in the context of patient cohort identification for comparative effectiveness studies and clinical decision support applications. The goal is to derive clinically meaningful distance metric to measure the similarity between patients represented by their key clinical indicators. How to incorporate physician feedback with regard to the retrieval results? How to interactively update the underlying similarity measure based on the feedback? Moreover, often different physicians have different understandings of patient similarity based on their patient cohorts. The distance metric learned for each individual physician often leads to a limited view of the true underlying distance metric. How to integrate the 45 individual distance metrics from each physician into a globally consistent unified metric? We describe a suite of supervised metric learning approaches that answer the above questions. In particular, we present Locally Supervised Metric Learning (LSML) to learn a generalized Mahalanobis distance that is tailored toward physician feedback. Then we describe the interactive metric learning (iMet) method that can incrementally update an existing metric based on physician feedback in an online fashion. To combine multiple similarity measures from multiple physicians, we present Composite Distance Integration (Comdi) method. In this approach we first construct discriminative neighbourhoods from each individual metrics, then combine them into a single optimal distance metric. Finally, we present a clinical decision support prototype system powered by the proposed patient similarity methods, and evaluate the proposed methods using real EHR data against several baselines. A. Sajid and H. Abbas, [8] The widespread deployment and utility of Wireless Body Area Networks (WBAN's) in healthcare systems required new technologies like Internet of Things (IoT) and cloud computing, that are able to deal with the storage and processing limitations of WBAN's. This amalgamation of WBAN-based healthcare systems to cloudbased healthcare systems gave rise to serious privacy concerns to the sensitive healthcare data. Hence, there is a need for the proactive identification and effective mitigation mechanisms for these patient's data privacy concerns that pose continuous threats to the integrity and stability of the healthcare environment. For this purpose, a systematic literature review has been conducted that presents a clear picture of the privacy concerns of patient's data in cloud-assisted healthcare systems and analysed the mechanisms that are recently proposed by the research community. The methodology used for conducting the review was based on Kitchenham guidelines. Results from the review show that most of the patient's data privacy techniques do not fully address the privacy concerns and therefore require more efforts. The summary presented in this paper would help in setting research directions for the techniques and mechanisms that are needed to address the patient's data privacy concerns in a balanced and light-weight manner by considering all the aspects and limitations of the cloud-assisted healthcare systems. Sachchidanand Singh ; Nirmala Singh[8] Blockchain is a decentralized ledger used to securely exchange digital currency, perform deals and transactions. Each member of the network has access to the latest copy of encrypted ledger so that they can validate a new transaction. Blockchain ledger is a collection of all Bitcoin transactions executed in the past. Basically, it's a distributed database which maintains a continuously growing tamper proof data structure blocks which holds batches of individual transactions. The completed blocks are added in a linear and chronological order. Each block contains a timestamp and information link which points to a previous block. Bitcoin is peer-to-peer permission-less network which allows every user to connect to the network and send new transaction to verify and create new blocks. Satoshi Nakamoto described design of Bitcoin digital currency in his research paper posted to cryptography listserv in 2008. Nakamoto's suggestion has solved long pending problem of ISSN 2277-5099 | © 2019 Bonfring Bonfring International Journal of Software Engineering and Soft Computing, Vol. 9, No. 2, April 2019 cryptographers and laid the foundation stone for digital currency. This paper explains the concept, characteristics, need of Blockchain and how Bitcoin works. It attempts to highlights role of Blockchain in shaping the future of banking, financial institutions and adoption of Internet of Things(IoT). III. METHODOLOGY 3.1 Client Data Encryption We utilize the model presented in, and take the advantage of NTRU to protect the client’s physiological data from being leaked or abused. This scheme is to protect the user’s privacy when transmitting the data from the smartphone to the cloudlet. 3.2 Cloudlet based Data Sharing Typically, users geographically close to each other connect to the same cloudlet. It’s likely for them to share common aspects, for example, patients suffer from similar kind of disease exchange information of treatment and share related data. For this purpose, we use users’ similarity and reputation as input data. After we obtain users’ trust levels, a certain threshold is set for the comparison. Once reaching or exceeding the threshold, it is considered that the trust between the users is enough for data sharing. Otherwise, the data will not share with low trust level. 3.3 Remote Cloud Data Privacy Protection Compared to user’s daily data in cloudlet, the data stored in remote contain larger scale medical data, e.g., EMR, which will be stored for a long term. We use the methods presented to divide EMR into explicit identifier (EID), quasi-identifier (QID) and medical information (MI). After classifying, proper protection is given for the data containing users’ sensitive information. 3.4 Collaborative Ids based on Cloudlet Mesh There is a vast volume of medical data stored in the remote cloud, it is critical to apply security mechanism to protect the database from malicious intrusions. In this paper, we develop specific countermeasures to establish a defence system for the large medical database in the remote cloud storage. Specifically, collaborative IDS based on the cloudlet mesh structure is used to screen any visit to the database as a protection border. If the detection shows a malicious intrusion in advance, the collaborative IDS will fire an alarm and block the visit, and vice-versa. The collaborative IDS, as a guard of the cloud database, can protect a vast number of medical data and make sure of the security of the database. 46 IV. CONCLUSION In this paper, we investigated the problem of privacy protection and sharing large medical data in cloudlets and the remote cloud. We developed a system which does not allow users to transmit data to the remote cloud in consideration of secure collection of data, as well as low communication cost. However, it does allow users to transmit data to a cloudlet, which triggers the data sharing problem in the cloudlet. Firstly, we can utilize wearable devices to collect users’ data, and in order to protect users privacy, we use NTRU mechanism to make sure the transmission of users’ data to cloudlet in security. Secondly, for the purpose of sharing data in the cloudlet, we use trust model to measure users trust level to judge level to share data or not. Thirdly, for privacypreserving of remote cloud data, we partition the data stored in the remote cloud and encrypt the data in different ways, so as to not just ensure data protection but also accelerate the efficacy of transmission. Finally, we propose collaborative IDS based on cloudlet mesh to protect the whole system with block chain technology. The proposed schemes are validated with simulations and experiments. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] M.S. Hossain, “Cloud-supported cyber–physical localization framework for patients monitoring”, IEEE Systems Journal, Vol. 11, No. 1, Pp. 118127, 2015. K.T. Pickard and M. Swan, “Big desire to share big health data: A shift in consumer attitudes toward personal health information”, In AAAI Spring Symposium Series, 2014. M.S. Hossain, G. Muhammad, M.F. Alhamid, B. Song and K. AlMutib, “Audio-visual emotion recognition using big data towards 5g”, Mobile Networks and Applications, Pp. 1–11, 2016. J.J. Yang, J. Li, J. Mulder, Y. 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