scholarly journals Decentralized Personal Health Records with Interacting Physician Apps and Intelligent Agents on Blockchain and Smart Contract Technologies (Preprint)

2020 ◽  
Author(s):  
Hyeong-Joon ­Kim ◽  
Hye Hyun Kim ◽  
Hosuk Ku ◽  
Kyung Don Yoo ◽  
Suehyun Lee ◽  
...  
Keyword(s):  
Data Sharing ◽  
Intelligent Agents ◽  
Data Privacy ◽  
Data Integrity ◽  
Health Data ◽  
Smart Devices ◽  
Data Element ◽  
Element Level ◽  
Smart Contract ◽  
Transaction Manager

BACKGROUND The Health Avatar Platform (HAP) provides a mobile health environment with interconnected patient Avatars, physician apps, and intelligent agents (IoA3) for data privacy and participatory medicine. However, its fully decentralized architecture has come at the expense of decentralized data management and data provenance. OBJECTIVE The introduction of blockchain and smart contract (SC) technologies to the HAP legacy platform with a clinical metadata registry (MDR) remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data. METHODS HAP decentralizes patient data in appropriate locations with no central storage, i.e., on patients’ smartphones and on physicians’ smart devices. We implemented an Ethereum-based hash chain for all transactions and SC-based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into three SCs, in this case a health data transaction manager, a transaction status manager, and an API transaction manager. The actual decentralized health data are managed in off-chain manner on their appropriate smart devices and authenticated by hashed metadata on-chain. RESULTS Metadata of each data transaction are captured in a HAP blockchain node by the SCs. We provide workflow diagrams each of the three use cases of data push (from a physician app or an intelligent Agents to a patient Avatar), data pull (requested to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash chained metadata support data element-level verification of the data integrity in subsequent transactions. SCs can incentivize transactions for data sharing and intelligent digital healthcare services. CONCLUSIONS HAP and IoA3 provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions by SCs.

scholarly journals The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare

2019 ◽  
Vol 28 (01) ◽  
pp. 195-202 ◽  
Author(s):  
Marc Cuggia ◽  
Stéphanie Combes
Keyword(s):  
Data Sharing ◽  
Medical Informatics ◽  
Data Privacy ◽  
Data Use ◽  
Health Data ◽  
The European Union ◽  
Global Approach ◽  
Data Governance ◽  
Academic Hospitals ◽  
Access Data

Objective: The diversity and volume of health data have been rapidly increasing in recent years. While such big data hold significant promise for accelerating discovery, data use entails many challenges including the need for adequate computational infrastructure and secure processes for data sharing and access. In Europe, two nationwide projects have been launched recently to support these objectives. This paper compares the French Health Data Hub initiative (HDH) to the German Medical Informatics Initiatives (MII). Method: We analysed the projects according to the following criteria: (i) Global approach and ambitions, (ii) Use cases, (iii) Governance and organization, (iv) Technical aspects and interoperability, and (v) Data privacy access/data governance. Results: The French and German projects share the same objectives but are different in terms of methodologies. The HDH project is based on a top-down approach and focuses on a shared computational infrastructure, providing tools and services to speed projects between data producers and data users. The MII project is based on a bottom-up approach and relies on four consortia including academic hospitals, universities, and private partners. Conclusion: Both projects could benefit from each other. A Franco-German cooperation, extended to other countries of the European Union with similar initiatives, should allow sharing and strengthening efforts in a strategic area where competition from other countries has increased.

scholarly journals A 21st Century Embarrassment of Riches: The Balance Between Health Data Access, Usage, and Sharing

2018 ◽  
Vol 27 (01) ◽  
pp. 005-006 ◽  
Author(s):  
John Holmes ◽  
Lina Soualmia ◽  
Brigitte Séroussi
Keyword(s):  
Data Sharing ◽  
21St Century ◽  
Data Privacy ◽  
Data Access ◽  
Health Data ◽  
Editorial Team ◽  
Special Topic ◽  
Related Data ◽  
Health Related ◽  
Different Sources

Objectives: To provide an introduction to the 2018 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: This editorial provides an overview and introduction to the 2018 IMIA Yearbook which special topic is: “Between access and privacy: Challenges in sharing health data”. The special topic editors and section are discussed, and the new section of the 2018 Yearbook, Cancer Informatics, is introduced. Changes in the Yearbook editorial team are also described. Results: With the exponential burgeoning of health-related data, and attendant demands for sharing and using these data, the special topic for 2018 is noteworthy for its timeliness. Data sharing brings responsibility for preservation of data privacy, and for this, patient perspectives are of paramount importance in understanding how patients view their health data and how their privacy should be protected. Conclusion: With the increase in availability of health-related data from many different sources and contexts, there is an urgent need for informaticians to become aware of their role in maintaining the balance between data sharing and privacy.

A Systematic Review of Privacy Preserving Healthcare Data Sharing on Blockchain

2020 ◽  
pp. 31-37
Author(s):  
Mustafa Tanriverdi ◽  
Keyword(s):  
Data Sharing ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Health Data ◽  
Health Records ◽  
Healthcare Data ◽  
Shared Data ◽  
Quality Of Health Services ◽  
Electronic Health

Sharing the electronic health data helps to increase the accuracy of the diagnoses and to improve the quality of health services. This shared data can also be used in medical research and can reduce medical costs. However, health data are fragmented across decentralized hospitals, this prevents data sharing and puts patients’ privacy at risks. In recent years, blockchain has revealed solutions that make life easier in many areas thanks to its distributed, safe and immutable structure. There are many blockchain-based studies in the literature on providing data privacy and sharing in different areas. In some studies, blockchain has been used with technologies such as cloud computing and cryptology. In the field of healthcare blockchain-based solutions are offered for the management and sharing of Electronic health records. In these solutions, private and consortium blockchain types are generally preferred and Public Key Infrastructure (PKI) and encryption are used for data privacy. Within the scope of this study, blockchain-based studies on the privacy preserving data sharing of health data were examined. In this paper, information about the studies in the literature and potential issues that can be studied in the future were discussed. In addition, information about current blockchain technologies such as smart contracts and PKI is also given.

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

scholarly journals Privacy, security, and the public health researcher in the era of electronic health record research

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Neal D Goldstein ◽  
Anand D Sarwate
Keyword(s):  
Public Health ◽  
Data Privacy ◽  
Large Scale ◽  
Health Data ◽  
Shared Responsibility ◽  
Privacy And Security ◽  
The Public ◽  
Health Researcher ◽  
Public Health Researcher ◽  
Electronic Health

Health data derived from electronic health records are increasingly utilized in large-scale population health analyses. Going hand in hand with this increase in data is an increasing number of data breaches. Ensuring privacy and security of these data is a shared responsibility between the public health researcher, collaborators, and their institutions. In this article, we review the requirements of data privacy and security and discuss epidemiologic implications of emerging technologies from the computer science community that can be used for health data. In order to ensure that our needs as researchers are captured in these technologies, we must engage in the dialogue surrounding the development of these tools.

scholarly journals Community Attitudes Towards Sharing Government Health Data with Private Companies: A Scoping Review

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Jackie Street ◽  
Belinda Fabrianesi ◽  
Rebecca Bosward ◽  
Stacy Carter ◽  
Annette Braunack-Mayer
Keyword(s):  
Data Sharing ◽  
Health Care Providers ◽  
Scoping Review ◽  
Public Attitudes ◽  
Health Data ◽  
Public Confidence ◽  
Care Providers ◽  
Private Industry ◽  
Good Communication ◽  
Government Health

IntroductionLarge volumes of health data are generated through the interaction of individuals with hospitals, government agencies and health care providers. There is potential in the linkage and sharing of administrative data with private industry to support improved drug and device provision but data sharing is highly contentious. Objectives and ApproachWe conducted a scoping review of quantitative and qualitative studies examining public attitudes towards the sharing of health data, held by government, with private industry for research and development. We searched four data bases, PubMed, Scopus, Cinahl and Web of Science as well as Google Scholar and Google Advanced. The search was confined to English-only publications since January 2014 but was not geographically limited. We thematically coded included papers. ResultsWe screened 6788 articles. Thirty-six studies were included primarily from UK and North America. No Australian studies were identified. Across studies, willingness to share non-identified data was generally high with the participant’s own health provider (84-91%) and academic researchers (64-93%) but fell if the data was to be shared with private industry (14-53%). There was widespread misunderstanding of the benefits of sharing data for health research. Publics expressed concern about a range of issues including data security, misuse of data and use of data to generate profit. Conditions which would increase public confidence in sharing of data included: strict safeguards on data collection and use including secure storage, opt-in or opt-out consent mechanisms, and good communication through trusted agents. Conclusion / ImplicationsWe identified a research gap: Australian views on sharing government health data with private industry. The international experience suggests that public scepticism about data sharing with private industry will need to be addressed by good communication about public benefit of data sharing, a strong program of public engagement and information sharing conducted through trusted entities.

scholarly journals Evaluation of Privacy Risks of Patients’ Data in China: Case Study

10.2196/13046 ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. e13046 ◽  
Author(s):  
Mengchun Gong ◽  
Shuang Wang ◽  
Lezi Wang ◽  
Chao Liu ◽  
Jianyang Wang ◽  
...  
Keyword(s):  
Health Care ◽  
Data Sharing ◽  
Data Privacy ◽  
Patient Data ◽  
Chinese Patients ◽  
Biomedical Data ◽  
Safe Harbor ◽  
Patient Privacy ◽  
Privacy Risk ◽  
Privacy Risks

Background Patient privacy is a ubiquitous problem around the world. Many existing studies have demonstrated the potential privacy risks associated with sharing of biomedical data. Owing to the increasing need for data sharing and analysis, health care data privacy is drawing more attention. However, to better protect biomedical data privacy, it is essential to assess the privacy risk in the first place. Objective In China, there is no clear regulation for health systems to deidentify data. It is also not known whether a mechanism such as the Health Insurance Portability and Accountability Act (HIPAA) safe harbor policy will achieve sufficient protection. This study aimed to conduct a pilot study using patient data from Chinese hospitals to understand and quantify the privacy risks of Chinese patients. Methods We used g-distinct analysis to evaluate the reidentification risks with regard to the HIPAA safe harbor approach when applied to Chinese patients’ data. More specifically, we estimated the risks based on the HIPAA safe harbor and limited dataset policies by assuming an attacker has background knowledge of the patient from the public domain. Results The experiments were conducted on 0.83 million patients (with data field of date of birth, gender, and surrogate ZIP codes generated based on home address) across 33 provincial-level administrative divisions in China. Under the Limited Dataset policy, 19.58% (163,262/833,235) of the population could be uniquely identifiable under the g-distinct metric (ie, 1-distinct). In contrast, the Safe Harbor policy is able to significantly reduce privacy risk, where only 0.072% (601/833,235) of individuals are uniquely identifiable, and the majority of the population is 3000 indistinguishable (ie the population is expected to share common attributes with 3000 or less people). Conclusions Through the experiments based on real-world patient data, this work illustrates that the results of g-distinct analysis about Chinese patient privacy risk are similar to those from a previous US study, in which data from different organizations/regions might be vulnerable to different reidentification risks under different policies. This work provides reference to Chinese health care entities for estimating patients’ privacy risk during data sharing, which laid the foundation of privacy risk study about Chinese patients’ data in the future.

scholarly journals An extensive research survey on data integrity and deduplication towards privacy in cloud storage

2020 ◽  
Vol 10 (2) ◽  
pp. 2011
Author(s):  
Anil Kumar G. ◽  
Shantala C. P.
Keyword(s):  
Cloud Storage ◽  
Data Privacy ◽  
Storage System ◽  
Data Integrity ◽  
Data Deduplication ◽  
Research Issues ◽  
Existing Problems ◽  
Research Survey ◽  
Challenging Tasks ◽  
Future Direction

Owing to the highly distributed nature of the cloud storage system, it is one of the challenging tasks to incorporate a higher degree of security towards the vulnerable data. Apart from various security concerns, data privacy is still one of the unsolved problems in this regards. The prime reason is that existing approaches of data privacy doesn't offer data integrity and secure data deduplication process at the same time, which is highly essential to ensure a higher degree of resistance against all form of dynamic threats over cloud and internet systems. Therefore, data integrity, as well as data deduplication is such associated phenomena which influence data privacy. Therefore, this manuscript discusses the explicit research contribution toward data integrity, data privacy, and data deduplication. The manuscript also contributes towards highlighting the potential open research issues followed by a discussion of the possible future direction of work towards addressing the existing problems.

Security, Privacy, and Ownership Issues With the Use of Wearable Health Technologies

2018 ◽  
pp. 1068-1083
Author(s):  
Don Kerr ◽  
Kerryn Butler-Henderson ◽  
Tony Sahama
Keyword(s):  
Data Privacy ◽  
Wearable Technology ◽  
Health Data ◽  
Security And Privacy ◽  
Primary Concern ◽  
Health Technologies ◽  
State Security ◽  
Data Ownership ◽  
The Future ◽  
Data Theft

When considering the use of mobile or wearable health technologies to collect health data, a majority of users state security and privacy of their data is a primary concern. With users being connected 24/7, there is a higher risk today of data theft or the misappropriate use of health data. Furthermore, data ownership is often a misunderstood topic in wearable technology, with many users unaware who owns the data collected by a device, what that data can be used for and who can receive that data. Many countries are reviewing privacy governance in an attempt to clarify data privacy and ownership. But is it too late? This chapter explores the concepts of security and privacy of data from mobile and wearable technology, with specific examples, and the implications for the future.

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