Blockchain Papers

Lingyu Bian, Zhang Li, Kai Zhao, Fei Shi

No abstract is available for this record.

Daming Li, Wenjian Liu, Lianbing Deng, Bin Qin

Abstract Blockchain is a hash chain with chain structure formed by data blocks in chronological order. With the promotion of bitcoin, the research and application of blockchain, the underlying core technology of bitcoin, is growing rapidly, which is considered to be the fifth generation of Internet disruptive technology after mobile Internet. At present, there are various identity management applications and platforms based on blockchain, but while blockchain has gradually become the cornerstone of the value Internet, it also brings great challenges to the privacy protection needs of users. This article analyzes the privacy protection mechanism of blockchain network layer, transaction layer and application layer, including malicious node detection and access restriction technology in network layer, mixed currency technology and encryption technology in blockchain transaction layer. In this article, distributed communicable network technology and multimedia data security technology are used to build a blockchain security system. We designed the efficient system to accurately analyze the data and the information protection. The experimental results show that the proposed method has better data security guarantee ability.

Jinbeom Seo, Youngbok Cho

In this paper, we use the blockchain to manage our own medical information on our own and to maintain data integrity where data can be changed without our approval. This paper proposes a system that can solve the problem of costly repetition of expensive medical imaging and make more accurate diagnosis by sharing one image with various experts.

Alexander Loktyushin, Kai Herz, Nhan Tam Dang, Felix Glang · 10 authors

Purpose: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction without human knowledge on MR strategies. This enables a target-based optimization from scratch, as well as exploration of novel and flexible MR sequence strategies. Methods: The entire scanning and reconstruction process is simulated end-to-end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T1 and T2, and B0. As proof of concept we use both conventional MR images but also binary masks and T1 maps as a target and optimize from scratch using the loss defined by data fidelity, SAR, and scan time. Results: In a first attempt, MRzero learns all gradient and RF events from zero, and is able to generate the aimed at target image. Appending a neural network layer to the reconstruction module also arbitrary targets were learned successfully. Experiments could be translated to image acquisition at a real system (3T Siemens, PRISMA) and could be verified in measurements of phantoms and the human brain in vivo. Discussion/Conclusion: We have developed a fully automated MR sequence generator based on Bloch equation simulations and supervised learning. While we focus on the method herein, having such a differentiable digital MR twin at hand paves the way to a novel way of generating MR sequence and reconstruction solely governed by the target provided, which can be a certain MR contrast, but the possibilities for targets are limitless, e.g. quantification, segmentation, as well as contrasts of other image modalities.

Xiantao Jiang, F. Richard Yu, Tian Song, Zhaowei Ma · 6 authors

Object detection for autonomous driving is a huge challenge in the cross-domain adaptation scenario, especially for the time- and resource-consuming task. Distributed deep learning (DDL) has demonstrated a considerably good balance between efficiency and computation complexity. However, the reliability of DDL is low. Moreover, the cost of training data and model is not priced well. In this article, a novel blockchain-enabled model sharing approach is proposed to improve the performance of object detection with cross-domain adaptation for autonomous driving systems. Based on the blockchain and mobile-edge computing (MEC) technology, a domain-adaptive you-only-look-once (YOLOv2) model is trained across nodes, which can reduce significantly the domain discrepancy for different object categories. Furthermore, smart contracts are developed to perform data storage and model sharing tasks efficiently. The reliability of model sharing is ensured with blockchain consensus. We evaluate the proposed method under public data sets. The simulation results demonstrate that the efficiency and reliability of the proposed approach are better than the reference model.

Yichuan Wang, Xinyue Yin, He Zhu, Xinhong Hei

No abstract is available for this record.

Jignasha Dalal, Meenal Chaturvedi, Himani Gandre, Sanjana Thombare

No abstract is available for this record.

Tanweer Alam

The COVID-19 is an exponentially growing disease that has intentioned nations to use technologies to detect the coronavirus infection. Several nations are working greatly to fight against COVID-19. Many nations have been using a range of devices to combat the pandemic, seeking information about growth, monitoring as well as the leaking the confidential information of the residents. This research aims to assist infected people online using the Internet of Things (IoT) and Blockchain technologies through smart devices. IoT-based healthcare devices gather useful information, provide additional insight through symptoms and behaviors, allow remote monitoring, and simply give people better self - determination and healthcare. Blockchain allows the secure transfer of patient health information, regulates the medical distribution network. A four-layer architecture is proposed using IoT and Blockchain to detect and prevent individuals to be COVID 19. This research provides a framework for patients with COVID-19 infectious disease and recognizes health issues and diagnoses online. Smart devices such as smartphones can install any mobile apps such as Aarogya Setu, Tawakkalna, and so on. These applications can track COVID-19 patients properly. The installation of mobile apps on smart devices focuses to reduce the time and cost and increase the performance of the infectious patient’s condition. A four-layer architecture is proposed using IoT and Blockchain technologies. Many research works focus on investigating, analyzing, and highlighting the affected individuals through guiding the COVID-19 infection. Eventually, various mobile apps are recognized and addressed in this paper.

Prerna Gulati, Aditi Sharma, Kartikey Bhasin, Chandrashekhar Azad

No abstract is available for this record.

Jin Wang, Wencheng Chen, Lei Wang, R. Simon Sherratt · 6 authors

As the number of sensor network application scenarios continues to grow, the security problems inherent in this approach have become obstacles that hinder its wide application. However, it has attracted increasing attention from industry and academia. The blockchain is based on a distributed network and has the characteristics of nontampering and traceability of block data. It is thus naturally able to solve the security problems of the sensor networks. Accordingly, this paper first analyzes the security risks associated with data storage in the sensor networks, then proposes using blockchain technology to ensure that data storage in the sensor networks is secure. In the traditional blockchain, the data layer uses a Merkle hash tree to store data; however, the Merkle hash tree cannot provide non-member proof, which makes it unable to resist the attacks of malicious nodes in networks. To solve this problem, this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and nonmember proof. Moreover, the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements. This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model. Finally, this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.

Qinnan Zhang, Jianming Zhu, Yuchen Wang

No abstract is available for this record.

Joseph Migga Kizza

No abstract is available for this record.

Pronaya Bhattacharya, Sudeep Tanwar, Umesh Bodkhe, Sudhanshu Tyagi · 5 authors

Electronic Health Records (EHRs) allows patients to control, share, and manage their health records among family members, friends, and healthcare service providers using an open channel, i.e., Internet. Thus, privacy, confidentiality, and data consistency are major challenges in such an environment. Although, cloud-based EHRs addresses the aforementioned discussions, but these are prone to various malicious attacks, trust management, and non-repudiation among servers. Hence, blockchain-based EHR systems are most popular to create the trust, security, and privacy among healthcare users. Motivated from the aforementioned discussions, we proposes a framework called as Blockchain-Based Deep Learning as-a-Service (BinDaaS). It integrates blockchain and deep-learning techniques for sharing the EHR records among multiple healthcare users and operates in two phases. In the first phase, an authentication and signature scheme is proposed based on lattices-based cryptography to resist collusion attacks among N-1 healthcare authorities from N. In the second phase, Deep Learning as-a-Service (DaaS) is used on stored EHR datasets to predict future diseases based on current indicators and features of patient. The obtained results are compared using various parameters such as accuracy, end-to-end latency, mining time, and computation and communication costs in comparison to the existing state-of-the-art proposals. From the results obtained, it is inferred that BinDaaS outperforms the other existing proposals with respect to the aforementioned parameters.

You Jun Kim, Choong Seon Hong

Federated learning (FL) is a decentralized learning method that deviated from the conventional centralized learning. The FL progresses learning locally on each device and gradually improves the learning model through interaction with the central server. However, it can cause network overload because of limited communication bandwidth and the participation of a huge number of users. One of the ways to minimize the network load is for the model to converge rapidly and stably with target learning accuracy. In this paper, we propose blockchain based federated learning scenario. Blockchain can efficiently induce users to participate in learning and can separate each participating user as a `node'. In addition, it can be pursued the integrity, stability, and so on. We consider two types of weights to choose the subset of clients for updating the global model. First, we consider the weight based on local learning accuracy of each client. Second, we consider the weight based on participation frequency of each client. We choose two key performance indicators, learning speed and standard deviation, to compare the performance of our proposed scheme with existing schemes. The simulation results show that our proposed scheme achieves higher stability along with fast convergence time for targeted accuracy compared to others.

Meng Shen, Yawen Deng, Liehuang Zhu, Xiaojiang Du · 5 authors

With the advent of medical IoT devices, the types and volumes of medical images have significantly increased. Retrieving of medical images is of great importance to facilitate disease diagnosis and improve treatment efficiency. However, it may raise privacy concerns from individuals, since medical images contain patients' sensitive and private information. Existing studies on retrieval of medical data either fail to protect sensitive information of medical images or are limited to a single image data provider. In this article, we propose a blockchain-based system for medical image retrieval with privacy protection. We first describe the typical scenarios of medical image retrieval and summarize the corresponding requirements in system design. Using the emerging blockchain techniques, we present the layered architecture and threat model of the proposed system. In order to accommodate large-size images with storage-constrained blocks, we capture a carefully selected feature vector from each medical image and design a customized transaction structure, which protects the privacy of medical images and image features. We also discuss the challenges and opportunities of future research.

Ahmet Buğday, Adnan Özsoy, Serdar Murat Öztaner, Hayri Sever

No abstract is available for this record.

Ahmed Faeq Hussein, Abbas K. AlZubaidi, Qais Ahmed Habash, Mustafa Musa Jaber

A crucial role is played by personal biomedical data when it comes to maintaining proficient access to health records by patients as well as health professionals. However, it is difficult to get a unified view pertaining to health data that have been scattered across various health centers/hospital sections. To be specific, health records are distributed across many places and cannot be integrated easily. In recent years, blockchain has arisen as a promising solution that helps to achieve the sharing of individual biomedical information in a secure way, whilst also having the benefit of privacy preservation because of its immutability. This research puts forward a blockchain-based managing scheme that helps to establish interpretation improvements pertaining to electronic biomedical systems. In this scheme, two blockchains were employed to construct the base, whereby the second blockchain algorithm was used to generate a secure sequence for the hash key that was generated in first blockchain algorithm. This adaptive feature enables the algorithm to use multiple data types and also combines various biomedical images and text records. All data, including keywords, digital records, and the identity of patients, are private key encrypted with a keyword searching function so as to maintain data privacy, access control, and a protected search function. The obtained results, which show a low latency (less than 750 ms) at 400 requests/second, indicate the possibility of its use within several health care units such as hospitals and clinics.

Md. Abdur Rahman, Mamunur Rashid, Stuart J. Barnes, M. Shamim Hossain · 6 authors

Once a subject is diagnosed with cancer, a patient goes through a series of diagnosis and tests, referred to as after cancer treatment. Due to the nature of the treatment and side effects on regular lifestyles, maintaining quality of life in the home environment is a challenging task. Sometimes within a home environment, a cancer patient's situation changes abruptly, as the functionality of certain organs deteriorate, which affects their quality of life. In this paper, we propose a Blockchain and off-chain based framework which will allow multiple medical and ambient intelligent IoT sensors to capture quality of life information from one's home environment and securely share it with one's community of interest. Using our proposed framework, both transactional records and multimedia big data - consisting of a user's physiological as well as mental states - can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed Blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. We further designed a generic oncologist smart contract and digital wallet for different stakeholders to automate the treatment plan of a particular patient. Finally, we will present our current implementation status, which provides significant encouragement for further development.

Harshita Khandelwal, Kritika Mittal, Shreeyaa Agrawal, Harsh Jain

During the course of education the students achieve many certificates. Student produce these certificates while applying for jobs at public or private sectors, where all these certificates are needed to be verified manually. There can be incidents where students may produce the fake certificate and it is difficult to identify them. This problem of fake academic certificates has been a longstanding issue in the academic community. Because it is possible to create such certificates at low cost and the process to verify them is very complex, as they are manually needed to be verified. This problem can be solved by storing the digital certificates on the Blockchain. The Blockchain technology provides immutability and publicly verifiable transactions, these properties of Blockchain can be used to generate the digital certificate which are anti-counterfeit and easy to verify.

Vijayakumar, V., K.M. Sabarivelan, J. Tamizhselvan, B Ranjith · 5 authors

Blockchain is a decentralized network technology. Blockchain consists of a number of blocks. The blocks are connected with other through a chain. Hence the name Blockchain. A block consists of a number of transactions. The links between the blocks are made up of hash values. The hash values are calculated using the transactions in a block and the hash value of the previous block. Healthcare is one of the biggest industry. It also remains as a industry which lacks transparency. At crucial situations the patients medical reports are not readily available. Interoperability between medical organizations is not available due to trust issues. Blockchain is a technology which can provide trust and transparency to its participants. Combining blockchain with healthcare can bring a huge change in the healthcare domain. By including frameworks like Hyperledger, we can provide an industrial standard to healthcare industry along with transparency and trust. All the medical data can be stored in a distributed ledger, which can be used at critical periods for examining report details. Blockchain also provides high security to the data. The data in the blockchain will remain tamper proof.

Lixin Yang, Genshe Chen, Ronghua Xu, Sherry Chen · 5 authors

Imaging studies are one of the leading drivers of modern medical decision making, and thus, their accessibility to healthcare providers and patients is of critical importance. However, current techniques for storage and transferring medical imaging data are inconvenient and sometimes wholly inadequate. In this paper, we propose a decentralized autonomous medical image processing approach using blockchain technology. Blockchain will enable the sharing of key relevant data using a distributed, decentralized, shared ledger that is available to participants. We outline a framework that utilizes blockchain to enable users to access imaging data in a secure and autonomous manner. A user case is experimentally investigated to validate our proposed approach.

Amin Fadaeddini, Babak Majidi, Mohammad Eshghi

No abstract is available for this record.

Daniel P Mera

In today’s world, the Blockchain technology is used for different purposes has brought an increment in the development of different Blockchain platforms, services, and utilities for storing data securely and efficiently. Quorum Blockchain, an Ethereum fork created by JPMorgan Chase, has placed itself in one of the widely used, efficient and trustful Blockchain platforms available today. Because of the importance which Quorum is contributing to the world, it is important to test and measure different aspects of the platform, not only to prove how efficient the software can be but as well as to have a clear view on what type of environment the platform could be better used. This research has been focused on testing the efficiency and speed of the transactions been sent to the platform in different types of environments, like local server nodes, virtual machine nodes and cloud instance nodes, by focusing its tests on the average of transactions per second or TPS being made. Once the tests were carried out, and results were obtained, there was a clear difference between the environments used. Using cloud instances for nodes improves the TPS for every single type of transactions over any other types of nodes. It might have been predicted that Cloud Instances would have done a better performance, but other factors could have caused the fallback in results for this type of nodes.

Sarita Thapa, Papri Ghosh, Bijoy Chhetri

No abstract is available for this record.