Privacy Preservation in Patient Information Exchange (PIE) Systems based on Blockchain: System Design (Preprint)

2021 ◽  
Author(s):  
Sejong ­Lee ◽  
Jaehyeon Kim ◽  
Yongseok Kwon ◽  
Teasung Kim ◽  
Sunghyun Cho
Keyword(s):  
Access Control ◽  
Data Sharing ◽  
Medical Records ◽  
Information Exchange ◽  
Privacy Preservation ◽  
Medical Information ◽  
High Reliability ◽  
Medical System ◽  
Distributed Data ◽  
High Scalability

BACKGROUND Blockchain is a distributed storage technology that provides a powerful tamper-proof technique through a distributed ledger and decentralized network. Initially, blockchain was primarily used for cryptocurrency in the financial field. However, it has attracted attention in various fields such as media, logistics, and medical care. Notably, various studies are being conducted to use blockchain in the medical field, where data reliability and integrity are essential. Representative medical blockchain research includes decentralized medical system design, secure data sharing schemes, and access control for privacy-preservation while sharing electronic medical records (EMRs). OBJECTIVE Our goal is to design a blockchain-based EMR sharing system that provides high reliability and scalability so that electronic medical records can be shared safely and efficiently. The system protects patients' privacy in medical data through a medical information exchange process that includes data encryption and access control. METHODS We propose a blockchain-based EMR sharing system that allows patients to manage the medical records scattered across multiple hospitals and share them with other users. Our patient information exchange (PIE) chain protects the patient's EMR from security threats such as counterfeiting and privacy issues during data sharing. Also, it guarantees high scalability by using distributed data sharing methods to share regardless of the size or type of EMR quickly. To check the proposed system's performance, we performed a simulation of the EMR sharing process and compared it with previous works on blockchain-based medical systems. RESULTS The simulation model is implemented using Hyperledger Fabric, an open source blockchain framework. Experimental results show that it takes an average of 10.1 ms to download 1MB of EMR on the proposed system. Moreover, it provides high scalability as it can rapidly share various data, regardless of size and type. The proposed system proposes a distributed ledger structure and a security level-based access control scheme to prevent data forgery attacks by a malicious user and unauthorized access. Moreover, it ensures high reliability by preventing data loss and privacy leakage due to sniffing and spoofing attacks with a data re-encryption scheme. CONCLUSIONS This paper proposes the PIE system, a Medical system that guarantees high reliability and scalability. The PIE system protects the EMR of the Patient created in the medical service process from threats such as personal information leakage and forgery. Through the distributed data sharing process based on blockchain, the EMR of the Patient can be quickly shared regardless of the data size. Our contribution paves the way for a patient-centered EMR sharing environment to integrate and manage patient medical information through the proposed blockchain-based Medical system.

scholarly journals Lightweight Privacy-Preserving Data Sharing Scheme for Internet of Medical Things

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhuo Zhao ◽  
Chingfang Hsu ◽  
Lein Harn ◽  
Qing Yang ◽  
Lulu Ke
Keyword(s):  
Access Control ◽  
Data Sharing ◽  
Medical Information ◽  
High Efficiency ◽  
Privacy Preserving ◽  
Medical Data ◽  
Medical Professionals ◽  
Sharing Scheme ◽  
Internet Of Medical Things ◽  
Similar Solutions

Internet of Medical Things (IoMT) is a kind of Internet of Things (IoT) that includes patients and medical sensors. Patients can share real-time medical data collected in IoMT with medical professionals. This enables medical professionals to provide patients with efficient medical services. Due to the high efficiency of cloud computing, patients prefer to share gathering medical information using cloud servers. However, sharing medical data on the cloud server will cause security issues, because these data involve the privacy of patients. Although recently many researchers have designed data sharing schemes in medical domain for security purpose, most of them cannot guarantee the anonymity of patients and provide access control for shared health data, and further, they are not lightweight enough for IoMT. Due to these security and efficiency issues, a novel lightweight privacy-preserving data sharing scheme is constructed in this paper for IoMT. This scheme can achieve the anonymity of patients and access control of shared medical data. At the same time, it satisfies all described security features. In addition, this scheme can achieve lightweight computations by using elliptic curve cryptography (ECC), XOR operations, and hash function. Furthermore, performance evaluation demonstrates that the proposed scheme takes less computation cost through comparison with similar solutions. Therefore, it is fairly an attractive solution for efficient and secure data sharing in IoMT.

scholarly journals Proposal for a Secure Data Sharing and Processing in Cloud Applications for Healthcare Domain

2021 ◽  
Vol 3 (1) ◽  
pp. 10-17
Author(s):  
Mbarek Marwan ◽  
Ali Karti ◽  
Hassan Ouahmane
Keyword(s):  
Access Control ◽  
Information Exchange ◽  
Data Exchange ◽  
Medical Information ◽  
Health Sector ◽  
Medical Data ◽  
Cloud Services ◽  
It Services ◽  
Wide Range ◽  
Cloud Applications

Information Technology (IT) services have become an inherent component in almost all sectors. Similarly, the health sector has been recently integrating IT to meet the growing demand for medical data exchange and storage. Currently, cloud has become a real hosting alternative for traditional on-permise software. In this model, not only do health organizations have access to a wide range of services but most importantly they are charged based on the usage of these cloud applications. However, especially in the healthcare domain, cloud computing deems challenging as to the sensitivity of health data. This work aims at improving access to medical data and securely sharing them across healthcare professionals, allowing real-time collaboration. From these perspectives, they propose a hybrid cryptosystem based on AES and Paillier to prevent the disclosure of confidential data, as well as computing encrypted data. Unlike most other solutions, the proposed framework adopts a proxy-based architecture to tackle some issues regarding privacy concerns and access control. Subsequently, this system typically guarantees that only authorized users can view or use specific resources in a computing environment. To this aim, they use eXtensible Access Control Markup Language (XACML) standard to properly design and manage access control policies. In this study, they opt for the (Abbreviated Language for Authorization) ALFA tool to easily formulate XACML policies and define complex rules. The simulation results show that the proposal offers simple and efficient mechanisms for the secure use of cloud services within the healthcare domain. Consequently, this framework is an appropriate method to support collaboration among all entities involved in medical information exchange.

scholarly journals Blockchain-based platforms for genomic data sharing: a de-centralized approach in response to the governance problems?

2018 ◽  
Vol 26 (1) ◽  
pp. 76-80 ◽  
Author(s):  
Mahsa Shabani
Keyword(s):  
Access Control ◽  
Data Sharing ◽  
Genomic Data ◽  
Data Access ◽  
Distributed Data ◽  
Data Ownership ◽  
Governance Challenges ◽  
Data Stewardship

Abstract Blockchain-based platforms are emerging to provide solutions for technical and governance challenges associated with genomic data sharing. Providing capabilities for distributed data stewardship and participatory access control along with effective ways for enforcement of the data access agreements and data ownership are among the major promises of these platforms.

Current Status of a Medical Information System

1970 ◽  
Vol 09 (03) ◽  
pp. 149-160 ◽  
Author(s):  
E. Van Brunt ◽  
L. S. Davis ◽  
J. F. Terdiman ◽  
S. Singer ◽  
E. Besag ◽  
...  
Keyword(s):  
Information System ◽  
Medical Records ◽  
Medical Information ◽  
Clinical Laboratory ◽  
Development Program ◽  
Medical Data ◽  
Direct Access ◽  
Medical Information System ◽  
Current Status ◽  
Central System

A pilot medical information system is being implemented and currently is providing services for limited categories of patient data. In one year, physicians’ diagnoses for 500,000 office visits, 300,000 drug prescriptions for outpatients, one million clinical laboratory tests, and 60,000 multiphasic screening examinations are being stored in and retrieved from integrated, direct access, patient computer medical records.This medical information system is a part of a long-term research and development program. Its major objective is the development of a multifacility computer-based system which will support eventually the medical data requirements of a population of one million persons and one thousand physicians. The strategy employed provides for modular development. The central system, the computer-stored medical records which are therein maintained, and a satellite pilot medical data system in one medical facility are described.

J 5 — A Data Processing System for Medical Information

1967 ◽  
Vol 06 (01) ◽  
pp. 1-6
Author(s):  
P. Hall ◽  
Ch. Mellner ◽  
T. Danielsson
Keyword(s):  
Medical Records ◽  
Medical Information ◽  
Health Care Systems ◽  
Processing System ◽  
Linear Pattern ◽  
Flexible Form ◽  
Care Systems ◽  
Pattern Recognition Analysis ◽  
Numeric Information

A system for medical information has been developed. The system is a general and flexible one which without reprogramming or new programs can accept any alphabetic and/or numeric information. Coded concepts and natural language can be read, stored, decoded and written out. Medical records or parts of records (diagnosis, operations, therapy, laboratory tests, symptoms etc.) can be retrieved and selected. The system can process simple statistics but even make linear pattern recognition analysis.The system described has been used for in-patients, outpatients and individuals in health examinations.The use of computers in hospitals, health examinations or health care systems is a problem of storing information in a general and flexible form. This problem has been solved, and now it is possible to add new routines like booking and follow-up-systems.

Methods for detecting and correcting contextual data quality problems

2021 ◽  
Vol 25 (4) ◽  
pp. 763-787
Author(s):  
Alladoumbaye Ngueilbaye ◽  
Hongzhi Wang ◽  
Daouda Ahmat Mahamat ◽  
Ibrahim A. Elgendy ◽  
Sahalu B. Junaidu
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Web Applications ◽  
Support Vector ◽  
Similarity Metrics ◽  
Distributed Data ◽  
Contextual Data ◽  
Formal Taxonomy ◽  
E Learning ◽  
High Scalability

Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

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