scholarly journals Healthchain: A Privacy Protection System for Medical Data Based on Blockchain

2021 ◽  
Vol 13 (10) ◽  
pp. 247
Author(s):  
Baocheng Wang ◽  
Zetao Li
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Single Point ◽  
Medical Data ◽  
Protection System ◽  
Security And Privacy ◽  
Group Signature ◽  
Single Server ◽  
Medical Institutions ◽  
Server Architecture

Recently, with the great development of e-health, more and more countries have made certain achievements in the field of electronic medical treatment. The digitization of medical equipment and the structuralization of electronic medical records are the general trends. While bringing convenience to people, the explosive growth of medical data will further promote the value of mining medical data. Obviously, finding out how to safely store such a large amount of data is a problem that urgently needs to be solved. Additionally, the particularity of medical data makes it necessarily subject to great privacy protection needs. This reinforces the importance of designing a safe solution to ensure data privacy. Many existing schemes are based on single-server architecture, which have some natural defects (such as single-point faults). Although blockchain can help solve such problems, there are still some deficiencies in privacy protection. To solve these problems, this paper designs a medical data privacy protection system, which integrates blockchain, group signature, and asymmetric encryption to realize reliable medical data sharing between medical institutions and protect the data privacy of patients. This paper proves theoretically that it meets our security and privacy requirements, and proves its practicability through system implementation.

Oracle Data Privacy Protection System of Virtual Database

2021 ◽  
pp. 353-363
Author(s):  
Shenglong Liu ◽  
Hongbin Zhu ◽  
Tao Zhao ◽  
Heng Wang ◽  
Xianzhou Gao ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Protection System ◽  
Virtual Database ◽  
Data Privacy Protection

scholarly journals Privacy-Protection Scheme Based on Sanitizable Signature for Smart Mobile Medical Scenarios

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhiyan Xu ◽  
Min Luo ◽  
Neeraj Kumar ◽  
Pandi Vijayakumar ◽  
Li Li
Keyword(s):  
Data Security ◽  
Privacy Protection ◽  
Medical Data ◽  
Security And Privacy ◽  
Smart Devices ◽  
Protection Scheme ◽  
Mobile Medicine ◽  
Time Space ◽  
Medical Network ◽  
Smart Mobile

With the popularization of wireless communication and smart devices in the medical field, mobile medicine has attracted more and more attention because it can break through the limitations of time, space, and objects and provide more efficient and quality medical services. However, the characteristics of a mobile smart medical network make it more susceptible to security threats such as data integrity damage and privacy leakage than those of traditional wired networks. In recent years, many digital signature schemes have been proposed to alleviate some of these challenges. Unfortunately, traditional digital signatures cannot meet the diversity and privacy requirements of medical data applications. In response to this problem, this paper uses the unique security attributes of sanitizable signatures to carry out research on the security and privacy protection of medical data and proposes a data security and privacy protection scheme suitable for smart mobile medical scenarios. Security analysis and performance evaluation show that our new scheme effectively guarantees data security and user privacy while greatly reducing computation and communication costs, making it especially suitable for mobile smart medical application scenarios.

scholarly journals Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation (Preprint)

2019 ◽  
Author(s):  
Rulin Shao ◽  
Hongyu He ◽  
Ziwei Chen ◽  
Hui Liu ◽  
Dianbo Liu
Keyword(s):  
Distributed System ◽  
Data Privacy ◽  
High Performance ◽  
Privacy Preservation ◽  
Characteristic Curve ◽  
Model Development ◽  
Medical Data ◽  
Performance Model ◽  
Security And Privacy ◽  
Sensitive Information

BACKGROUND Artificial neural networks have achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data collection is often prevented by privacy concerns, and people want to take control over their sensitive information during both the training and using processes. OBJECTIVE To address security and privacy issues, we propose a privacy-preserving method for the analysis of distributed medical data. The proposed method, termed stochastic channel-based federated learning (SCBFL), enables participants to train a high-performance model cooperatively and in a distributed manner without sharing their inputs. METHODS We designed, implemented, and evaluated a channel-based update algorithm for a central server in a distributed system. The update algorithm will select the channels with regard to the most active features in a training loop, and then upload them as learned information from local datasets. A pruning process, which serves as a model accelerator, was further applied to the algorithm based on the validation set. RESULTS We constructed a distributed system consisting of 5 clients and 1 server. Our trials showed that the SCBFL method can achieve an area under the receiver operating characteristic curve (AUC-ROC) of 0.9776 and an area under the precision-recall curve (AUC-PR) of 0.9695 with only 10% of channels shared with the server. Compared with the federated averaging algorithm, the proposed SCBFL method achieved a 0.05388 higher AUC-ROC and 0.09695 higher AUC-PR. In addition, our experiment showed that 57% of the time is saved by the pruning process with only a reduction of 0.0047 in AUC-ROC performance and a reduction of 0.0068 in AUC-PR performance. CONCLUSIONS In this experiment, our model demonstrated better performance and a higher saturating speed than the federated averaging method, which reveals all of the parameters of local models to the server. The saturation rate of performance could be promoted by introducing a pruning process and further improvement could be achieved by tuning the pruning rate.

scholarly journals The issues connected with the anonymization of medical data. Part 1. The introduction to the anonymization of medical data. Ensuring the protection of sensitive information with the use of such methods as f(a) and f(a,b)

2014 ◽  
Vol 8 (1) ◽  
pp. 13-21 ◽  
Author(s):  
ARKADIUSZ LIBER
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Differential Privacy ◽  
Medical Data ◽  
Medical Documentation ◽  
Sensitive Information ◽  
Sensitive Data ◽  
New Methods ◽  
Anonymized Data ◽  
Data Privacy Protection

Introduction: Medical documentation must be protected against damage or loss, in compliance with its integrity and credibility and the opportunity to a permanent access by the authorized staff and, finally, protected against the access of unauthorized persons. Anonymization is one of the methods to safeguard the data against the disclosure.Aim of the study: The study aims at the analysis of methods of anonymization, the analysis of methods of the protection of anonymized data and the study of a new security type of privacy enabling to control sensitive data by the entity which the data concerns.Material and methods: The analytical and algebraic methods were used.Results: The study ought to deliver the materials supporting the choice and analysis of the ways of the anonymization of medical data, and develop a new privacy protection solution enabling the control of sensitive data by entities whom this data concerns.Conclusions: In the paper, the analysis of solutions of data anonymizing used for medical data privacy protection was con-ducted. The methods, such as k-Anonymity, (X,y)- Anonymity, (a,k)- Anonymity, (k,e)-Anonymity, (X,y)-Privacy, LKC-Privacy, l-Diversity, (X,y)-Linkability, t-Closeness, Confidence Bounding and Personalized Privacy were described, explained and analyzed. The analysis of solutions to control sensitive data by their owners was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the anonimized data protection was conducted, in particular the methods of: d-Presence, e-Differential Privacy, (d,g)-Privacy, (a,b)-Distributing Privacy and protections against (c,t)-Isolation were analyzed. The author introduced a new solution of the controlled protection of privacy. The solution is based on marking a protected field and multi-key encryption of the sensitive value. The suggested way of fields marking is in accordance to the XML standard. For the encryption (n,p) different key cipher was selected. To decipher the content the p keys of n is used. The proposed solution enables to apply brand new methods for the control of privacy of disclosing sensitive data.

Security and Privacy for Electronic Healthcare Records Using AI in Blockchain

2021 ◽  
pp. 90-102
Author(s):  
Ramani Selvanambi ◽  
Samarth Bhutani ◽  
Komal Veauli
Keyword(s):  
Data Privacy ◽  
Correct Diagnosis ◽  
Medical Data ◽  
Medical Advice ◽  
Data Availability ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Related Data ◽  
Healthcare Data ◽  
Health Related

In yesteryears, the healthcare data related to each patient was limited. It was stored and controlled by the hospital authorities and was seldom regulated. With the increase in awareness and technology, the amount of medical data per person has increased exponentially. All this data is essential for the correct diagnosis of the patient. The patients also want access to their data to seek medical advice from different doctors. This raises several challenges like security, privacy, data regulation, etc. As health-related data are privacy-sensitive, the increase in data stored increases the risk of data exposure. Data availability and privacy are essential in healthcare. The availability of correct information is critical for the treatment of the patient. Information not easily accessed by the patients also complicates seeking medical advice from different hospitals. However, if data is easily accessible to everyone, it makes privacy and security difficult. Blockchains to store and secure data will not only ensure data privacy but will also provide a common method of data regulation.

scholarly journals A Medical Data Privacy Protection Scheme Based on Blockchain and Cloud Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Liang Huang ◽  
Hyung-Hyo Lee
Keyword(s):  
Cloud Computing ◽  
Data Sharing ◽  
Data Storage ◽  
Privacy Protection ◽  
Data Privacy ◽  
Medical Data ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Protection Scheme ◽  
Point To Point

With the features of decentralization and trustlessness and through distributed data storage, point-to-point transmission, and encryption algorithms, blockchain has shed new light on the security and protection of medical data, and it can resolve the contradiction between data sharing and privacy protection with proper security strategies. In this paper, we integrate the strengths of both blockchain and cloud computing and build the privacy protection scheme for medical data based on blockchain and cloud computing. This scheme introduces cloud computing and provides services to blockchain nodes with cloud server computing; meanwhile, it collects, analyzes, processes, and maintains medical data in the identity authentication interface and solves the insufficient computing abilities of some nodes in blockchain so as to verify the authenticity and reliability of data. The simulation experiment proves that the proposed scheme is effective. It can achieve the secure protection and integrity verification of medical data and address the problems of high computing complexity, data sharing, and privacy protection.

scholarly journals IOV Privacy Protection System Based on Double-Layered Chains

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yin Ru Chen ◽  
Jin Rui Sha ◽  
Zhi Hong Zhou
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Communication ◽  
Protection System ◽  
Chain Model ◽  
Security Risk ◽  
States Of Affairs ◽  
Zero Knowledge ◽  
Vehicle Data ◽  
Cost Efficient

As the Internet of Vehicle (IOV) being widely applied throughout our daily life, how to secure data privacy of each vehicle is nowadays a hot topic. Taking an aim of solving this problem, a privacy protection system on double-layered chain basis is designed to eliminate the said security risk during vehicle data communication. At the same time, the nontampering nature of the block chain is used to realize reasonable arbitration in traffic accident disputes, vehicle insurance claims, and other states of affairs. Specifically, an IOV double-layered chain model is constructed to simulate a semicentralized system that is convenient for government to supervise; also, a RSA protocol based on zero-knowledge proof (ZKP) is designed to bring safety and zero-knowledge property to the system; finally, we give the application scenario of this IOV privacy protection system based on double-layered chain that it can be widely used in vehicle-sharing industry. The communication costs, respectively, under double-layered chain and single-layered chain frameworks, are compared to prove that the double-layered structure does save cost. Thus an IOV privacy scheme that is safer and more cost-efficient is given.

scholarly journals Fusion Chain: A Decentralized Lightweight Blockchain for IoT Security and Privacy

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 391
Author(s):  
Dongjun Na ◽  
Sejin Park
Keyword(s):  
Data Privacy ◽  
Single Point ◽  
Consensus Algorithm ◽  
Security And Privacy ◽  
Public Key ◽  
Byzantine Fault Tolerance ◽  
Blockchain Technology ◽  
Private Data ◽  
Security Problem ◽  
Iot Devices

As the use of internet of things (IoT) devices increases, the importance of security has increased, because personal and private data such as biometrics, images, photos, and voices can be collected. However, there is a possibility of data leakage or manipulation by monopolizing the authority of the data, since such data are stored in a central server by the centralized structure of IoT devices. Furthermore, such a structure has a potential security problem, caused by an attack on the server due to single point vulnerability. Blockchain’s, through their decentralized structure, effectively solve the single point vulnerability, and their consensus algorithm allows network participants to verify data without any monopolizing. Therefore, blockchain technology becomes an effective solution for solving the security problem of the IoT’s centralized method. However, current blockchain technology is not suitable for IoT devices. Blockchain technology requires large storage space for the endless append-only block storing, and high CPU processing power for performing consensus algorithms, while its opened block access policy exposes private data to the public. In this paper, we propose a decentralized lightweight blockchain, named Fusion Chain, to support IoT devices. First, it solves the storage size issue of the blockchain by using the interplanetary file system (IPFS). Second, it does not require high computational power by using the practical Byzantine fault tolerance (PBFT) consensus algorithm. Third, data privacy is ensured by allowing only authorized users to access data through public key encryption using PKI. Fusion Chain was implemented from scratch written using Node.js and golang. The results show that the proposed Fusion Chain is suitable for IoT devices. According to our experiments, the size of the blockchain dramatically decreased, and only 6% of CPU on an ARM core, and 49 MB of memory, is used on average for the consensus process. It also effectively protects privacy data by using a public key infrastructure (PKI).

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