scholarly journals The Need for Machine-Processable Agreements in Health Data Management

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 87
Author(s):  
George Konstantinidis ◽  
Adriane Chapman ◽  
Mark J. Weal ◽  
Ahmed Alzubaidi ◽  
Lisa M. Ballard ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Management ◽  
Data Sharing ◽  
Medical Information ◽  
Service Providers ◽  
Personal Data ◽  
Health Data ◽  
Coarse Grained ◽  
Semantic Web Technologies ◽  
Fine Grained

Data processing agreements in health data management are laid out by organisations in monolithic “Terms and Conditions” documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and do not allow for more than a few opt-in or opt-out options for individuals to express their consent on personal data processing, and these options often do not transfer to software as they were intended to. In this paper, we study the problem of health data sharing and we advocate the need for individuals to describe their personal contract of data usage in a formal, machine-processable language. We develop an application for sharing patient genomic information and test results, and use interactions with patients and clinicians in order to identify the particular peculiarities a privacy/policy/consent language should offer in this complicated domain. We present how Semantic Web technologies can have a central role in this approach by providing the formal tools and features required in such a language. We present our ongoing approach to construct an ontology-based framework and a policy language that allows patients and clinicians to express fine-grained consent, preferences or suggestions on sharing medical information. Our language offers unique features such as multi-party ownership of data or data sharing dependencies. We evaluate the landscape of policy languages from different areas, and show how they are lacking major requirements needed in health data management. In addition to enabling patients, our approach helps organisations increase technological capabilities, abide by legal requirements, and save resources.

scholarly journals CrowdMed-II: a blockchain-based framework for efficient consent management in health data sharing

2022 ◽  
Author(s):  
Chaochen Hu ◽  
Chao Li ◽  
Guigang Zhang ◽  
Zhiwei Lei ◽  
Mira Shah ◽  
...  
Keyword(s):  
Data Sharing ◽  
Service Providers ◽  
Heterogeneous Data ◽  
Health Data ◽  
Smart Contracts ◽  
Management Framework ◽  
Execution Costs ◽  
Traditional Health ◽  
Centralized Management ◽  
Health Service Providers

AbstractThe healthcare industry faces serious problems with health data. Firstly, health data is fragmented and its quality needs to be improved. Data fragmentation means that it is difficult to integrate the patient data stored by multiple health service providers. The quality of these heterogeneous data also needs to be improved for better utilization. Secondly, data sharing among patients, healthcare service providers and medical researchers is inadequate. Thirdly, while sharing health data, patients’ right to privacy must be protected, and patients should have authority over who can access their data. In traditional health data sharing system, because of centralized management, data can easily be stolen, manipulated. These systems also ignore patient’s authority and privacy. Researchers have proposed some blockchain-based health data sharing solutions where blockchain is used for consensus management. Blockchain enables multiple parties who do not fully trust each other to exchange their data. However, the practice of smart contracts supporting these solutions has not been studied in detail. We propose CrowdMed-II, a health data management framework based on blockchain, which could address the above-mentioned problems of health data. We study the design of major smart contracts in our framework and propose two smart contract structures. We also introduce a novel search contract for searching patients in the framework. We evaluate their efficiency based on the execution costs on Ethereum. Our design improves on those previously proposed, lowering the computational costs of the framework. This allows the framework to operate at scale and is more feasible for widespread adoption.

scholarly journals Visualizing an Ethics Framework: A Method to Create Interactive Knowledge Visualizations From Health Policy Documents

10.2196/16249 ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. e16249
Author(s):  
Joanna Sleigh ◽  
Manuel Schneider ◽  
Julia Amann ◽  
Effy Vayena
Keyword(s):  
Data Processing ◽  
Data Sharing ◽  
Health Research ◽  
Qualitative Content Analysis ◽  
Research Misconduct ◽  
Personal Data ◽  
Knowledge Visualization ◽  
Ethical Guidelines ◽  
Policy Documents ◽  
Ethics Framework

Background Data have become an essential factor in driving health research and are key to the development of personalized and precision medicine. Primary and secondary use of personal data holds significant potential for research; however, it also introduces a new set of challenges around consent processes, privacy, and data sharing. Research institutions have issued ethical guidelines to address challenges and ensure responsible data processing and data sharing. However, ethical guidelines directed at researchers and medical professionals are often complex; require readers who are familiar with specific terminology; and can be hard to understand for people without sufficient background knowledge in legislation, research, and data processing practices. Objective This study aimed to visually represent an ethics framework to make its content more accessible to its stakeholders. More generally, we wanted to explore the potential of visualizing policy documents to combat and prevent research misconduct by improving the capacity of actors in health research to handle data responsibly. Methods We used a mixed methods approach based on knowledge visualization with 3 sequential steps: qualitative content analysis (open and axial coding, among others); visualizing the knowledge structure, which resulted from the previous step; and adding interactive functionality to access information using rapid prototyping. Results Through our iterative methodology, we developed a tool that allows users to explore an ethics framework for data sharing through an interactive visualization. Our results represent an approach that can make policy documents easier to understand and, therefore, more applicable in practice. Conclusions Meaningful communication and understanding each other remain a challenge in various areas of health care and medicine. We contribute to advancing communication practices through the introduction of knowledge visualization to bioethics to offer a novel way to tackle this relevant issue.

scholarly journals Making context the central concept in privacy engineering

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Tore Hoel ◽  
Weiqin Chen ◽  
Jan M. Pawlowski
Keyword(s):  
Data Sharing ◽  
Data Privacy ◽  
Service Providers ◽  
Design Science ◽  
Personal Data ◽  
Science Research ◽  
Design Science Research ◽  
Normative Theory ◽  
Contextual Integrity ◽  
Individual Privacy

Abstract There is a gap between people’s online sharing of personal data and their concerns about privacy. Till now, this gap is addressed by attempting to match individual privacy preferences with service providers’ options for data handling. This approach has ignored the role different contexts play in data sharing. This paper aims at giving privacy engineering a new direction putting context centre stage and exploiting the affordances of machine learning in handling contexts and negotiating data sharing policies. This research is explorative and conceptual, representing the first development cycle of a design science research project in privacy engineering. The paper offers a concise understanding of data privacy as a foundation for design extending the seminal contextual integrity theory of Helen Nissenbaum. This theory started out as a normative theory describing the moral appropriateness of data transfers. In our work, the contextual integrity model is extended to a socio-technical theory that could have practical impact in the era of artificial intelligence. New conceptual constructs such as ‘context trigger’, ‘data sharing policy’ and ‘data sharing smart contract’ are defined, and their application is discussed from an organisational and technical level. The constructs and design are validated through expert interviews; contributions to design science research are discussed, and the paper concludes with presenting a framework for further privacy engineering development cycles.

scholarly journals A Privacy-Preserving Attribute-Based Encryption System for Data Sharing in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xieyang Shen ◽  
Chuanhe Huang ◽  
Danxin Wang ◽  
Jiaoli Shi
Keyword(s):  
Data Sharing ◽  
Service Providers ◽  
Smart Cities ◽  
Information Leakage ◽  
Cloud Service ◽  
Data Access ◽  
Smart Devices ◽  
Fine Grained ◽  
Data Access Control ◽  
High Level

Information leakage and efficiency are the two main concerns of data sharing in cloud-aided IoT. The main problem is that smart devices cannot afford both energy and computation costs and tend to outsource data to a cloud server. Furthermore, most schemes focus on preserving the data stored in the cloud but omitting the access policy is typically stored in unencrypted form. In this paper, we proposed a fine-grained data access control scheme based on CP-ABE to implement access policies with a greater degree of expressiveness as well as hidden policies from curious cloud service providers. Moreover, to mitigate the extra computation cost generated by complex policies, an outsourcing service for decryption can be used by data users. Further experiments and extensive analysis show that we significantly decrease the communication and computation overhead while providing a high-level security scheme compared with the existing schemes.

scholarly journals Securing E-health Data using Ciphertext-Policy Attribute-Based Encryption with Dynamic User Revocation

2019 ◽  
Vol 8 (3) ◽  
pp. 7244-7250
Keyword(s):  
Access Control ◽  
Service Providers ◽  
Health Data ◽  
Security And Privacy ◽  
Unique Identifier ◽  
Fine Grained ◽  
Attribute Based Encryption ◽  
User Revocation ◽  
Role Based ◽  
Ciphertext Policy

E-health systems hold a massive amount of medical data that is stored and shared across healthcare service providers to deliver health facilities. However, security and privacy worries increase when sharing this data over distributed settings. As a result, Cryptography techniques have been considered to secure e-health data from unauthorized access. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) is commonly utilized in such a setting, which provides role-based and fine-grained access control over encrypted data. The CP-ABE suffers from the problem of user revocation where the entire policy must be changed even when only one user is revoked or removed from the policy. In this paper, we proposed a CP-ABE based access control model to support user revocation efficiently. Specifically, the proposed model associates a unique identifier to each user. This identifier is added to the policy attributes and removed dynamically when the user is added/revoked. A tree structure (PolicyPathTree) is designed specifically for our model. It can facilitate fast access to policy's attributes during the verification process; The model is analyzed using Information Theory Tools. Results show that our model outperforms other notable work in terms of computational overheads.,

scholarly journals Detection of Intrusion using Hybrid Feature Selection and Flexible Rule Based Machine Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 2852-2861
Keyword(s):  
Data Processing ◽  
Data Sharing ◽  
Service Providers ◽  
Attack Detection ◽  
Sensitive Data ◽  
Characteristics Analysis ◽  
Network Status ◽  
Legacy Applications ◽  
Status Analysis ◽  
Processing Framework

With the rapid growth in the data processing and data sharing, the application owners and the consumers of the applications are more influenced to use the remote storage on cloud-based data centre and the application generated data is also growing ups and bounds. Nevertheless, the adaptation of the data sharing, and data processing applications were not easy for the consumers. The application owners and the service providers have struggled with the sensitive data of the consumers and the consumers were also faced trust issues with the complete framework. The standard legacy applications were designed for the traditional centralized scenarios, where the intrusion detection can be performed only using the network status analysis and the application characteristics analysis. Moreover, most of the parallel calculations initially enhance the hybrid likelihood and change likelihood of GA as indicated by the populace advancement variable-based math and wellness esteem. Nevertheless, the population of data and the attacks on the data is high and the correct population size is highly difficult to determine. Regardless to mention, that the use of fitness functions will restrict the attack detection to certain types and these algorithms are bound to fail in case of a newer attack. However, with the migration of application to the data processing framework, the consumers have started demanding more security against the intrusions. A good number of research attempts were made to map the traditional security algorithms into the data processing space, nonetheless, the attempts were highly criticized due to the lack of proper analysis of security attacks on data processing applications. Hence, this work proposes a novel framework to detect the intrusions on data processing framework with justifying attack characteristics. This work proposes a novel algorithm to reduce the features of attack characteristics to justify the gaps on data processing frameworks with significant reduction in time for processing and further, proposes an algorithm to derive a strong rule engine to analyse the attack characteristics for detecting newer attacks. The complete proposed framework demonstrates nearly 93% and higher accuracy, which is much higher than the existing parallel research outcomes with least time complexity.

scholarly journals The “A” of FAIR – As Open as Possible, as Closed as Necessary

2020 ◽  
Vol 2 (1-2) ◽  
pp. 47-55 ◽  
Author(s):  
Annalisa Landi ◽  
Mark Thompson ◽  
Viviana Giannuzzi ◽  
Fedele Bonifazi ◽  
Ignasi Labastida ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Protection ◽  
Personal Data ◽  
Health Data ◽  
Privacy Rights ◽  
Regulatory Requirements ◽  
Data Set ◽  
General Data Protection Regulation ◽  
Digital Era ◽  
General Data

In order to provide responsible access to health data by reconciling benefits of data sharing with privacy rights and ethical and regulatory requirements, Findable, Accessible, Interoperable and Reusable (FAIR) metadata should be developed. According to the H2020 Program Guidelines on FAIR Data, data should be “as open as possible and as closed as necessary”, “open” in order to foster the reusability and to accelerate research, but at the same time they should be “closed” to safeguard the privacy of the subjects. Additional provisions on the protection of natural persons with regard to the processing of personal data have been endorsed by the European General Data Protection Regulation (GDPR), Reg (EU) 2016/679, that came into force in May 2018. This work aims to solve accessibility problems related to the protection of personal data in the digital era and to achieve a responsible access to and responsible use of health data. We strongly suggest associating each data set with FAIR metadata describing both the type of data collected and the accessibility conditions by considering data protection obligations and ethical and regulatory requirements. Finally, an existing FAIR infrastructure component has been used as an example to explain how FAIR metadata could facilitate data sharing while ensuring protection of individuals.

A Fine-Grained Stateful Data Analytics Method Based on Resilient State Table

2018 ◽  
Vol 10 (2) ◽  
pp. 66-79 ◽  
Author(s):  
Jike Ge ◽  
Wenbo He ◽  
Zuqin Chen ◽  
Can Liu ◽  
Jun Peng ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Model ◽  
Data Analytics ◽  
Programming Model ◽  
Data Access ◽  
Coarse Grained ◽  
Memory Representation ◽  
State Table ◽  
Fine Grained ◽  
Application Fields

This article describes how stateful data analytic frameworks have emerged to provide fresh and low-latency results for big data processing. At present, it is desired to achieve the fine-grained data model in Spark data processing framework. However, Spark adopts coarse-grained data model in order to facilitate parallelization, it is challenging in dealing with the fine-grained data access in stateful data analytics. In this paper, the authors introduce a fine-grained stateful data component, Resilient State Table (RST), to Spark framework. For filling the gap between the coarse-grained data model in Spark and the fine-grained data access requirements in stateful data analytics, they devise the programming model of RST which interacts with Spark's coarse-grained memory representation seamlessly, and enable users to query/update the state entries in fine granularity with Spark-like programming interfaces. Performance evaluation experiments in various application fields demonstrate that their proposed solution achieves the improvements in latency, fault-tolerance, as well as scalability.

scholarly journals BSSPD: A Blockchain-Based Security Sharing Scheme for Personal Data with Fine-Grained Access Control

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Hongmin Gao ◽  
Zhaofeng Ma ◽  
Shoushan Luo ◽  
Yanping Xu ◽  
Zheng Wu
Keyword(s):  
Access Control ◽  
Data Sharing ◽  
Personal Data ◽  
Security And Privacy ◽  
Access Policy ◽  
Fine Grained ◽  
Sharing Scheme ◽  
Data Owner ◽  
Data User ◽  
Cloud Server

Privacy protection and open sharing are the core of data governance in the AI-driven era. A common data-sharing management platform is indispensable in the existing data-sharing solutions, and users upload their data to the cloud server for storage and dissemination. However, from the moment users upload the data to the server, they will lose absolute ownership of their data, and security and privacy will become a critical issue. Although data encryption and access control are considered up-and-coming technologies in protecting personal data security on the cloud server, they alleviate this problem to a certain extent. However, it still depends too much on a third-party organization’s credibility, the Cloud Service Provider (CSP). In this paper, we combined blockchain, ciphertext-policy attribute-based encryption (CP-ABE), and InterPlanetary File System (IPFS) to address this problem to propose a blockchain-based security sharing scheme for personal data named BSSPD. In this user-centric scheme, the data owner encrypts the sharing data and stores it on IPFS, which maximizes the scheme’s decentralization. The address and the decryption key of the shared data will be encrypted with CP-ABE according to the specific access policy, and the data owner uses blockchain to publish his data-related information and distribute keys for data users. Only the data user whose attributes meet the access policy can download and decrypt the data. The data owner has fine-grained access control over his data, and BSSPD supports an attribute-level revocation of a specific data user without affecting others. To further protect the data user’s privacy, the ciphertext keyword search is used when retrieving data. We analyzed the security of the BBSPD and simulated our scheme on the EOS blockchain, which proved that our scheme is feasible. Meanwhile, we provided a thorough analysis of the storage and computing overhead, which proved that BSSPD has a good performance.

scholarly journals Secure and Privacy-Preserving Data Sharing and Collaboration in Mobile Healthcare Social Networks of Smart Cities

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Qinlong Huang ◽  
Licheng Wang ◽  
Yixian Yang
Keyword(s):  
Social Networks ◽  
Data Sharing ◽  
Service Providers ◽  
Smart Cities ◽  
Data Encryption ◽  
Health Data ◽  
Security And Privacy ◽  
Broadcast Encryption ◽  
Social Data ◽  
Mobile Healthcare

Mobile healthcare social networks (MHSN) integrated with connected medical sensors and cloud-based health data storage provide preventive and curative health services in smart cities. The fusion of social data together with real-time health data facilitates a novel paradigm of healthcare big data analysis. However, the collaboration of healthcare and social network service providers may pose a series of security and privacy issues. In this paper, we propose a secure health and social data sharing and collaboration scheme in MHSN. To preserve the data privacy, we realize secure and fine-grained health data and social data sharing with attribute-based encryption and identity-based broadcast encryption techniques, respectively, which allows patients to share their private personal data securely. In order to achieve enhanced data collaboration, we allow the healthcare analyzers to access both the reencrypted health data and the social data with authorization from the data owner based on proxy reencryption. Specifically, most of the health data encryption and decryption computations are outsourced from resource-constrained mobile devices to a health cloud, and the decryption of the healthcare analyzer incurs a low cost. The security and performance analysis results show the security and efficiency of our scheme.

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