scholarly journals Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints: A Feasibility Study with COVID-19, X-Ray, and Cholesterol Dataset

2021 ◽  
Author(s):  
Yoo Jeong Ha ◽  
Gusang Lee ◽  
Minjae Yoo ◽  
Soyi Jung ◽  
Seehwan Yoo ◽  
...  
Keyword(s):  
Data Sharing ◽  
Learning Algorithm ◽  
Personal Information ◽  
Personal Data ◽  
Privacy Preserving ◽  
Medical Data ◽  
Learning Performance ◽  
Medical Systems ◽  
X Ray ◽  
Data Imbalance

Abstract It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven’t neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide groundbreaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient’s personal information. This paper provides a novel split learning algorithm coined the term, “multi-site split learning”, which enables a secure transfer of medical data between multiple hospitals without fear of exposing personal data contained in patient records. It also explores the effects of varying the number of end-systems and the ratio of data-imbalance on the deep learning performance. A guideline for the most optimal configuration of split learning that ensures privacy of patient data whilst achieving performance is empirically given. We argue the benefits of our multi-site split learning algorithm, especially regarding the privacy preserving factor, using CT scans of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.

Framework Design and Case Study for Privacy-Preserving Medical Data Publishing

2013 ◽  
Vol 4 (4) ◽  
pp. 48-65 ◽  
Author(s):  
Yu Niu ◽  
Ji-Jiang Yang ◽  
Qing Wang
Keyword(s):  
Data Sharing ◽  
Personal Information ◽  
Real Life ◽  
Privacy Preserving ◽  
Medical Data ◽  
Data Publishing ◽  
Free Text ◽  
Patient Privacy ◽  
Healthcare Data

With the pervasive using of Electronic Medical Records (EMR) and telemedicine technologies, more and more digital healthcare data are accumulated from multiple sources. As healthcare data is valuable for both commercial and scientific research, the demand of sharing healthcare data has been growing rapidly. Nevertheless, health care data normally contains a large amount of personal information, and sharing them directly would bring huge threaten to the patient privacy. This paper proposes a privacy preserving framework for medical data sharing with the view of practical application. The framework focuses on three key issues of privacy protection during the data sharing, which are privacy definition/detection, privacy policy management, and privacy preserving data publishing. A case study for Chinese Electronic Medical Record (ERM) publishing with privacy preserving is implemented based on the proposed framework. Specific Chinese free text EMR segmentation, Protected Health Information (PHI) extraction, and K-anonymity PHI anonymous algorithms are proposed in each component. The real-life data from hospitals are used to evaluate the performance of the proposed framework and system.

Framework Design and Case Study for Privacy-Preserving Medical Data Publishing

2015 ◽  
pp. 1115-1130
Author(s):  
Yu Niu ◽  
Ji-Jiang Yang ◽  
Qing Wang
Keyword(s):  
Data Sharing ◽  
Personal Information ◽  
Real Life ◽  
Privacy Preserving ◽  
Medical Data ◽  
Data Publishing ◽  
Patient Privacy ◽  
Multiple Sources ◽  
Healthcare Data

With the pervasive using of Electronic Medical Records (EMR) and telemedicine technologies, more and more digital healthcare data are accumulated from multiple sources. As healthcare data is valuable for both commercial and scientific research, the demand of sharing healthcare data has been growing rapidly. Nevertheless, health care data normally contains a large amount of personal information, and sharing them directly would bring huge threaten to the patient privacy. This paper proposes a privacy preserving framework for medical data sharing with the view of practical application. The framework focuses on three key issues of privacy protection during the data sharing, which are privacy definition/detection, privacy policy management, and privacy preserving data publishing. A case study for Chinese Electronic Medical Record (ERM) publishing with privacy preserving is implemented based on the proposed framework. Specific Chinese free text EMR segmentation, Protected Health Information (PHI) extraction, and K-anonymity PHI anonymous algorithms are proposed in each component. The real-life data from hospitals are used to evaluate the performance of the proposed framework and system.

scholarly journals SPChain: Blockchain-based medical data sharing and privacy-preserving eHealth system

2021 ◽  
Vol 58 (4) ◽  
pp. 102604
Author(s):  
Renpeng Zou ◽  
Xixiang Lv ◽  
Jingsong Zhao
Keyword(s):  
Data Sharing ◽  
Privacy Preserving ◽  
Medical Data

scholarly journals Efficient and Privacy-Preserving Medical Data Sharing in Internet of Things With Limited Computing Power

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 28019-28027 ◽  
Author(s):  
Dong Zheng ◽  
Axin Wu ◽  
Yinghui Zhang ◽  
Qinglan Zhao
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Privacy Preserving ◽  
Medical Data ◽  
Computing Power

Mutual Correlation-Based Anonymization for Privacy Preserving Medical Data Publishing

2018 ◽  
pp. 304-319
Author(s):  
Ashoka Kukkuvada ◽  
Poornima Basavaraju
Keyword(s):  
Privacy Preservation ◽  
Information Gain ◽  
Personal Data ◽  
Privacy Preserving ◽  
Medical Data ◽  
Innovative Approach ◽  
Data Publishing ◽  
Mutual Correlation ◽  
Data Utility ◽  
Data Elements

Currently the industry is focused on managing, retrieving, and securing massive amounts of data. Hence, privacy preservation is a significant concern for those organizations that publish/share personal data for vernacular analysis. In this chapter, the authors presented an innovative approach that makes use of information gain of the quasi attributes with respect to sensitive attributes for anonymizing the data, which gives the fruitfulness of an attribute in classifying the data elements, which is a two-way correlation among attributes. The authors show that the proposed approach preserves better data utility and has lesser complexity than former methods.

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 Research on electronic medical record access control based on blockchain

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988933 ◽  
Author(s):  
Yongbin Zhao ◽  
Meng Cui ◽  
Lijuan Zheng ◽  
Rui Zhang ◽  
Lili Meng ◽  
...  
Keyword(s):  
Access Control ◽  
Medical Record ◽  
Data Sharing ◽  
Electronic Medical Record ◽  
Medical Data ◽  
Role Based Access Control ◽  
Medical Systems ◽  
Control Research ◽  
Role Based ◽  
Medical Privacy

For the medical industry, there are problems such as poor sharing of medical data, tampering, and leakage of private data. In view of these problems, a blockchain-based electronic medical record access control research scheme based on the role-based access control model is proposed in this article. First, the appropriate access control strategy is adopted to solve the leakage problem of the user’s medical privacy information during the access process. Then, the information entropy technology is used to quantify the medical data, so that the medical data can be effectively and maximally utilized. Using the distributed general ledger characteristics of blockchain and its inherent security attributes, data islands can be eliminated, data sharing among medical systems can be promoted, access records can be prevented from being tampered with, and medical research and precise medical treatment can be better supported. Through this research, not only can user’s medical privacy information protection be realized during the service process but also patients can manage their own medical data autonomously, which is beneficial to privacy protection under the medical data sharing.

scholarly journals Privacy-aware blockchain for personal data sharing and tracking

2019 ◽  
Vol 9 (1) ◽  
pp. 80-91 ◽  
Author(s):  
Md Mehedi Hassan Onik ◽  
Chul-Soo Kim ◽  
Nam-Yong Lee ◽  
Jinhong Yang
Keyword(s):  
Information Management ◽  
Data Sharing ◽  
Management System ◽  
Personal Information ◽  
Personal Data ◽  
Information Management System ◽  
Data Movement ◽  
General Data Protection Regulation ◽  
Blockchain Technology ◽  
Personally Identifiable Information

AbstractSecure data distribution is critical for data accountability. Surveillance caused privacy breaching incidents have already questioned existing personal data collection techniques. Organizations assemble a huge amount of personally identifiable information (PII) for data-driven market analysis and prediction. However, the limitation of data tracking tools restricts the detection of exact data breaching points. Blockchain technology, an ‘immutable’ distributed ledger, can be leveraged to establish a transparent data auditing platform. However, Art. 42 and Art. 25 of general data protection regulation (GDPR) demands ‘right to forget’ and ‘right to erase’ of personal information, which goes against the immutability of blockchain technology. This paper proposes a GDPR complied decentralized and trusted PII sharing and tracking scheme. Proposed blockchain based personally identifiable information management system (BcPIIMS) demonstrates data movement among GDPR entities (user, controller and processor). Considering GDPR limitations, BcPIIMS used off-the-chain data storing architecture. A prototype was created to validate the proposed architecture using multichain. The use of off-the-chain storage reduces individual block size. Additionally, private blockchain also limits personal data leaking by collecting fast approval from restricted peers. This study presents personal data sharing, deleting, modifying and tracking features to verify the privacy of proposed blockchain based personally identifiable information management system.

Sign in / Sign up

Export Citation Format

Share Document