The recent approval of the General Data Protection Regulation (GDPR) imposes new data protection requirements on data controllers and processors with respect to the processing of European Union (EU) residents' data. These requirements consist of a single set of rules that have binding legal status and should be enforced in all EU member states. In light of these requirements, we propose in this paper the use of a blockchain-based approach to support data accountability and provenance tracking. Our approach relies on the use of publicly auditable contracts deployed in a blockchain that increase the transparency with respect to the access and usage of data. We identify and discuss three different models for our approach with different granularity and scalability requirements where contracts can be used to encode data usage policies and provenance tracking information in a privacy-friendly way. From these three models we designed, implemented, and evaluated a model where contracts are deployed by data subjects for each data controller, and a model where subjects join contracts deployed by data controllers in case they accept the data handling conditions. Our implementations show in practice the feasibility and limitations of contracts for the purposes identified in this paper.
Applied to the same field but with a more comprehensive approach is MedRec @cite_6 . The authors propose to give patients control over their Electronic Health Records (EHRs) through the use of Ethereum blockchain and smart contracts, which manage authentication, confidentiality, accountability and sharing of the data. The latter are referenced in the chain using their hashes but are stored externally. Miners are rewarded with anonymized aggregated data useful for research. Although the proposed approach addresses many issues, considerations about costs and scalability on a large scale deployment are not mentioned.
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Years of heavy regulation and bureaucratic inefficiency have slowed innovation for electronic medical records (EMRs). We now face a critical need for such innovation, as personalization and data science prompt patients to engage in the details of their healthcare and restore agency over their medical data. In this paper, we propose MedRec: a novel, decentralized record management system to handle EMRs, using blockchain technology. Our system gives patients a comprehensive, immutable log and easy access to their medical information across providers and treatment sites. Leveraging unique blockchain properties, MedRec manages authentication, confidentiality, accountability and data sharing -- crucial considerations when handling sensitive information. A modular design integrates with providers' existing, local data storage solutions, facilitating interoperability and making our system convenient and adaptable. We incentivize medical stakeholders (researchers, public health authorities, etc.) to participate in the network as blockchain &#x0022;miners&#x0022;. This provides them with access to aggregate, anonymized data as mining rewards, in return for sustaining and securing the network via Proof of Work. MedRec thus enables the emergence of data economics, supplying big data to empower researchers while engaging patients and providers in the choice to release metadata. The purpose of this short paper is to expose, prior to field tests, a working prototype through which we analyze and discuss our approach. Privacy concern is arguably the major barrier that hinders the deployment of electronic health record (EHR) systems which are considered more efficient, less error-prone, and of higher availability compared to traditional paper record systems. Patients are unwilling to accept the EHR system unless their protected health information (PHI) containing highly confidential data is guaranteed proper use and disclosure, which cannot be easily achieved without patients' control over their own PHI. However, cautions must be taken to handle emergencies in which the patient may be physically incompetent to retrieve the controlled PHI for emergency treatment. In this paper, we propose a secure EHR system, HCPP (Healthcaresystem for Patient Privacy), based on cryptographic constructions and existing wireless network infrastructures, to provide privacy protection to patients under any circumstances while enabling timelyPHI retrieval for life-saving treatment in emergency situations. Furthermore, our HCPP system restricts PHI access to authorized (not arbitrary) physicians, who can be traced and held accountable if the accessed PHI is found improperly disclosed. Last but not least, HCPP leverages wireless network access to support efficient and private storage retrieval of PHI, which underlies a secure and feasible EHR system. This paper addresses privacy issues in managing electronic health records by a third party cloud based service. Compared to traditional authentication-authorization mechanisms, the proposed approach minimizes the leakage of identity information of involved participants through unlinkability. Furthermore, it gives the ability to health record owners for making access control decisions. This solution employs an identity management scheme that enhances consumer privacy by preventing consumer profiling based on the credentials used to satisfy the service provider policies. The paper proposes a set of mechanisms to allow authenticated unlinkable access to electronic health records, while giving the record owners ability to make access control decisions. The security evaluation for accessing data in the cloud is detailed, and the implementation of the system is evaluated in this paper. In the Internet of Things (IoT) scenario, the block-chain and, in general, Peer-to-Peer approaches could play an important role in the development of decentralized and dataintensive applications running on billion of devices, preserving the privacy of the users. Our research goal is to understand whether the blockchain and Peer-to-Peer approaches can be employed to foster a decentralized and private-by-design IoT. As a first step in our research process, we conducted a Systematic Literature Review on the blockchain to gather knowledge on the current uses of this technology and to document its current degree of integrity, anonymity and adaptability. We found 18 use cases of blockchain in the literature. Four of these use cases are explicitly designed for IoT. We also found some use cases that are designed for a private-by-design data management. We also found several issues in the integrity, anonymity and adaptability. Regarding anonymity, we found that in the blockchain only pseudonymity is guaranteed. Regarding adaptability and integrity, we discovered that the integrity of the blockchain largely depends on the high difficulty of the Proof-of-Work and on the large number of honest miners, but at the same time a difficult Proof-of-Work limits the adaptability. We documented and categorized the current uses of the blockchain, and provided a few recommendations for future work to address the above-mentioned issues. The term electronic health record (EHR) refers to the complete set of information that resides in electronic form and is related to the past, present, and future health status or health care provided to a subject of care. The primary purpose of electronic health records (EHRs) is the documentation, retrieval, transmission, linking, and processing of multimedia information to legitimate users for the delivery of knowledge and decision support that enhance efficient and secure health-related services, regardless of the health-care model applied. Secondary uses of EHRs are related to policy development, education, research, quality management, and disease surveillance via anonymized datasets requiring, in most of the cases, explicit consent on behalf of the subject of care. Keywords: electronic health record architecture; health information infrastructure; reference information model Since the start of Bitcoin in 2008[1], blockchain technology emerged as the next revolutionary technology. Though blockchain started off as a core technology of Bitcoin, its use cases are expanding to many other areas including finances, Internet of Things (IoT), security and such[2]. Currently, many private and public sectors are diving into the technology[3]. Aside from that, as software and hardware improve, we would see the beginning of IoT. And those IoT devices need to communicate and synchronize with each other. But in situations where more than thousands or tens of thousands of IoT devices connected, we expect that using current model of server-client may have some limitations and issues while in synchronization. So, we propose using blockchain to build IoT system. Using blockchain, we can control and configure IoT devices. We manage keys using RSA public key cryptosystems where public keys are stored in Ethereum and private keys are saved on individual devices. Specifically, we choose Ethereum as our blockchain platform because using its smart contract, we can write our own Turing-complete code to run on top of Ethereum. Thus, we can easily manage configuration of IoT devices and build key management system. Even though we can simply use account as a key management system, which most of blockchain platform supports, we decide to use Ethereum because we can manage the system in a more fine-grained way. For the proof of a concept, we use a few IoT devices instead of a full system of IoT system, which consists of thousands of IoT devices. But in our later study, we would like to build a fully scaled IoT system using blockchain. The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHRs). While primarily designed for archiving patient information and performing administrative healthcare tasks like billing, many researchers have found secondary use of these records for various clinical informatics applications. Over the same period, the machine learning community has seen widespread advances in the field of deep learning. In this review, we survey the current research on applying deep learning to clinical tasks based on EHR data, where we find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification. We identify several limitations of current research involving topics such as model interpretability, data heterogeneity, and lack of universal benchmarks. We conclude by summarizing the state of the field and identifying avenues of future deep EHR research. Smart contracts are self-executing agreements written in program code and are envisioned to be one of the main applications of blockchain technology. While they are supported by prominent cryptocurrencies such as Ethereum, their further adoption is hindered by fundamental scalability challenges. For instance, in Ethereum contract execution suffers from a latency of more than 15 s, and the total number of contracts that can be executed per second is very limited. State channel networks are one of the core primitives aiming to address these challenges. They form a second layer over the slow and expensive blockchain, thereby enabling instantaneous contract processing at negligible costs. Despite technological advances, most smart objects in the Internet of Things (IoT) cannot be accessed using technologies designed and developed for interacting with powerful Internet servers. IoT use cases involve devices that not only have limited resources, but also they are not always connected to the Internet and are physically exposed to tampering. In this paper, we describe the design, development, and evaluation of a smart contract-based solution that allows end-users to securely interact with smart devices. Our approach enables access control, Thing authentication, and payments in a fully decentralized setting, taking at the same time into consideration the limitations and constraints imposed by both blockchain technologies and the IoT paradigm. Our prototype implementation is based on existing technologies, i.e., Ethereum smart contracts, which makes it realistic and fundamentally secure. One of the fundamental challenges that hinder further adaption of decentralized cryptocurrencies is scalability. Because current cryptocurrencies require that all transactions are processed and stored on a distributed ledger -- the so-called blockchain -- transaction throughput is inherently limited. An important proposal to significantly improve scalability are off-chain protocols, where the massive amount of transactions is executed without requiring the costly interaction with the blockchain. Examples of off-chain protocols include payment channels and networks, which are currently deployed by popular cryptocurrencies such as Bitcoin and Ethereum. A further extension of payment networks envisioned for cryptocurrencies are so-called state channel networks. In contrast to payment networks that only support off-chain payments between users, state channel networks allow execution of arbitrary complex smart contracts. The main contribution of this work is to give the first full specification for general state channel networks. Moreover, we provide formal security definitions and prove the security of our construction against powerful adversaries. An additional benefit of our construction is the use of channel virtualization, which further reduces latency and costs in complex channel networks. Abrupt changes in the miner hash rate applied to a proof-of-work (PoW) blockchain can adversely affect user experience and security. Because different PoW blockchains often share hashing algorithms, miners face a complex choice in deciding how to allocate their hash power among chains. We present an economic model that leverages Modern Portfolio Theory to predict a miner’s allocation over time using price data and inferred risk tolerance. The model matches actual allocations with mean absolute error within 20 for four out of the top five miners active on both Bitcoin (BTC) and Bitcoin Cash (BCH) blockchains. A model of aggregate allocation across those four miners shows excellent agreement in magnitude with the actual aggregate as well a correlation coefficient of 0.649. The accuracy of the aggregate allocation model is also sufficient to explain major historical changes in inter-block time (IBT) for BCH. Because estimates of miner risk are not time-dependent and our model is otherwise price-driven, we are able to use it to anticipate the effect of a major price shock on hash allocation and IBT in the BCH blockchain. Using a Monte Carlo simulation, we show that, despite mitigation by the new difficulty adjustment algorithm, a price drop of 50 could increase the IBT by 50 for at least a day, with a peak delay of 100 . The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide smart services with or without human intervention. While leveraging the large scale IoT based applications like Smart Gird or Smart Cities, IoTs also incur more concerns on privacy and security. Among the top security challenges that IoTs face, access authorization is critical in resource sharing and information protection. One of the weaknesses in today's access control (AC) is the centralized authorization server, which can be the performance bottleneck or the single point of failure. In this paper, BlendCAC, a blockchain enabled decentralized capability based AC is proposed for the security of IoTs. The BlendCAC aims at an effective access control processes to devices, services and information in large scale IoT systems. Based on the blockchain network, a capability delegation mechanism is suggested for access permission propagation. A robust identity based capability token management strategy is proposed, which takes advantage of smart contract for registering, propagation and revocation of the access authorization. In the proposed BlendCAC scheme, IoT devices are their own master to control their resources instead of being supervised by a centralized authority. Implemented and tested on a Raspberry Pi device and on a local private blockchain network, our experimental results demonstrate the feasibility of the proposed BlendCAC approach to offer a decentralized, scalable, lightweight and fine grained AC solution to IoT systems. Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose Dipole, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation. In the power grid, the Balance Responsible Parties (BRPs) purchase energy based on a forecast of the user consumption. The forecasts are imperfect, and the corrections of their real-time deviations are managed by a System Operator (SO), which charges the BRPs for the procured imbalances. Flexible consumers, associated with a BRP, can be involved in a demand response (DR) program to reduce the imbalance costs. However, running the DR program requires the BRP to invest resources in the infrastructure and increases its operating costs. To limit the intervention of BRP, we implement the DR via a blockchain smart contract. Moreover, to reduce the delay of publication of the imbalance price, caused by the inefficient accounting process of the current balancing markets, a second blockchain is adopted at the SO layer, procuring a fast and auditable credit settlements. The feasibility of the proposed architecture is evaluated over an Ethereum blockchain platform. The results show that block chains can enable a high automation of the balancing market, by providing (i) the implementation of aggregators with low operating cost and (ii) the timely and transparent access to the balancing information, thus fostering new business models for the BRPs. Ethereum is the second most valuable cryptocurrency today, with a current market cap of over $68B. What sets Ethereum apart from other cryptocurrencies is that it uses the blockchain to not only store a record of transactions, but also smart contracts and a history of calls made to those contracts. Thus, Ethereum represents a new form of distributed system: one where users can implement contracts that can provide functionality such as voting protocols, crowdfunding projects, betting agreements, and many more. However, despite the massive investment, little is known about how contracts in Ethereum are actually created and used. In this paper, we examine how contracts in Ethereum are created, and how users and contracts interact with one another. We modify the geth client to log all such interactions, and find that contracts today are three times more likely to be created by other contracts than they are by users, and that over 60 of contracts have never been interacted with. Additionally, we obtain the bytecode of all contracts and look for similarity; we find that less than 10 of user-created contracts are unique, and less than 1 of contract-created contracts are so. Clustering the contracts based on code similarity reveals even further similarity. These results indicate that there is substantial code re-use in Ethereum, suggesting that bugs in such contracts could have wide-spread impact on the Ethereum user population. Distributed m-healthcare cloud computing system significantly facilitates efficient patient treatment for medical consultation by sharing personal health information among healthcare providers. However, it brings about the challenge of keeping both the data confidentiality and patients’ identity privacy simultaneously. Many existing access control and anonymous authentication schemes cannot be straightforwardly exploited. To solve the problem, in this paper, a novel authorized accessible privacy model (AAPM) is established. Patients can authorize physicians by setting an access tree supporting flexible threshold predicates. Then, based on it, by devising a new technique of attribute-based designated verifier signature, a patient self-controllable multi-level privacy-preserving cooperative authentication scheme (PSMPA) realizing three levels of security and privacy requirement in distributed m-healthcare cloud computing system is proposed. The directly authorized physicians, the indirectly authorized physicians and the unauthorized persons in medical consultation can respectively decipher the personal health information and or verify patients’ identities by satisfying the access tree with their own attribute sets. Finally, the formal security proof and simulation results illustrate our scheme can resist various kinds of attacks and far outperforms the previous ones in terms of computational, communication and storage overhead. Most popular blockchain solutions, like Bitcoin, rely on proof-of-work, guaranteeing that the output of the consensus is agreed upon with high probability. However, this probability depends on the delivery of messages and that the computational power of the system is sufficiently scattered among pools of nodes in the network so that no pool can mine more blocks faster than the crowd. New approaches, like Ethereum, generalise the proof-of-work approach by letting individuals deploy their own private blockchain with high transaction throughput. As companies are starting to deploy private chains, it has become crucial to better understand the guarantees blockchains offer in such a small and controlled environment. In this paper, we present the , an execution that we experienced when building our private chain at NICTA Data61. Even though this anomaly has never been acknowledged before, it may translate into dramatic consequences for the user of blockchains. Named after the infamous Paxos anomaly, this anomaly makes dependent transactions, like "Bob sends money to Carole after he received money from Alice" impossible. This anomaly relies on the fact that existing blockchains do not ensure consensus safety deterministically: there is no way for Bob to make sure that Alice actually sent him coins without Bob using an external mechanism, like converting these coins into a fiat currency that allows him to withdraw. We also explore smart contracts as a potential alternative to transactions in order to freeze coins, and show implementations of smart contract that can suffer from the Blockchain anomaly and others that may cope with it. We introduce FairSwap -- an efficient protocol for fair exchange of digital goods using smart contracts. A fair exchange protocol allows a sender S to sell a digital commodity x for a fixed price p to a receiver R. The protocol is said to be secure if R only pays if he receives the correct x. Our solution guarantees fairness by relying on smart contracts executed over decentralized cryptocurrencies, where the contract takes the role of an external judge that completes the exchange in case of disagreement. While in the past there have been several proposals for building fair exchange protocols over cryptocurrencies, our solution has two distinctive features that makes it particular attractive when users deal with large commodities. These advantages are: (1) minimizing the cost for running the smart contract on the blockchain, and (2) avoiding expensive cryptographic tools such as zero-knowledge proofs. In addition to our new protocols, we provide formal security definitions for smart contract based fair exchange, and prove security of our construction. Finally, we illustrate several applications of our basic protocol and evaluate practicality of our approach via a prototype implementation for fairly selling large files over the cryptocurrency Ethereum. Energy storage units (ESUs) enable several attractive features of modern smart grids such as enhanced grid resilience, effective demand response, and reduced bills. However, uncoordinated charging of ESUs stresses the power system and can lead to a blackout. On the other hand, existing charging coordination mechanisms suffer from several limitations. First, the need for a central charging coordinator (CC) presents a single point of failure that jeopardizes the effectiveness of the charging coordination. Second, a transparent charging coordination mechanism does not exist where users are not aware whether the CC is honest or not in coordination charging requests among them in a fair way. Third, existing mechanisms overlook the privacy concerns of the involved customers. To address these limitations, in this paper, we leverage the blockchain and smart contracts to build a decentralized charging coordination mechanism without the need for a centralized charging coordinator. First ESUs should use tokens for anonymously authenticate themselves to the blockchain. Then each ESU sends a charging request that contains its State-of-Charge (SoC), Time-to-complete-charge (TCC) and amount of required charging to the smart contract address on the blockchain. The smart contract will then run the charging coordination mechanism in a self-executed manner such that ESUs with the highest priorities are charged in the present time slot while charging requests of lower priority ESUs are deferred to future time slots. In this way, each ESU can make sure that charging schedules are computed correctly. Finally, we have implemented the proposed mechanism on the Ethereum test-bed blockchain, and our analysis shows that execution cost can be acceptable in terms of gas consumption while enabling decentralized charging coordination with increased transparency, reliability, and privacy preserving. Objectives: We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods: Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results: 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions: Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts. Cli