Privacy Preserving Distributed Learning Clustering of HealthCare Data Using Cryptography Protocols

2010 ◽  
Author(s):  
Ahmed M. Elmisery ◽  
Huaiguo Fu
Keyword(s):  
Distributed Learning ◽  
Privacy Preserving ◽  
Healthcare Data

scholarly journals Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI

2021 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Gaia Ninatti ◽  
Daniele Loiacono ◽  
Edoardo Giacomello ◽  
...  
Keyword(s):  
Distributed Learning ◽  
Privacy Preserving

Anonymization Based on Improved Bucketization (AIB): A Privacy-Preserving Data Publishing Technique for Improving Data Utility in Healthcare Data

2021 ◽  
Vol 11 (12) ◽  
pp. 3164-3173
Author(s):  
R. Indhumathi ◽  
S. Sathiya Devi
Keyword(s):  
Medical Information ◽  
Threshold Value ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Published Data ◽  
Sensitive Information ◽  
Data Utility ◽  
Healthcare Data ◽  
Privacy Preserving Data Publishing ◽  
Horizontal Partitioning

Data sharing is essential in present biomedical research. A large quantity of medical information is gathered and for different objectives of analysis and study. Because of its large collection, anonymity is essential. Thus, it is quite important to preserve privacy and prevent leakage of sensitive information of patients. Most of the Anonymization methods such as generalisation, suppression and perturbation are proposed to overcome the information leak which degrades the utility of the collected data. During data sanitization, the utility is automatically diminished. Privacy Preserving Data Publishing faces the main drawback of maintaining tradeoff between privacy and data utility. To address this issue, an efficient algorithm called Anonymization based on Improved Bucketization (AIB) is proposed, which increases the utility of published data while maintaining privacy. The Bucketization technique is used in this paper with the intervention of the clustering method. The proposed work is divided into three stages: (i) Vertical and Horizontal partitioning (ii) Assigning Sensitive index to attributes in the cluster (iii) Verifying each cluster against privacy threshold (iv) Examining for privacy breach in Quasi Identifier (QI). To increase the utility of published data, the threshold value is determined based on the distribution of elements in each attribute, and the anonymization method is applied only to the specific QI element. As a result, the data utility has been improved. Finally, the evaluation results validated the design of paper and demonstrated that our design is effective in improving data utility.

Privacy Preserving in Digital Health

2021 ◽  
pp. 1503-1526
Author(s):  
Zakariae El Ouazzani ◽  
Hanan El Bakkali ◽  
Souad Sadki
Keyword(s):  
Information And Communication Technologies ◽  
Digital Health ◽  
Health Sector ◽  
Privacy Preserving ◽  
Health Data ◽  
Patient Privacy ◽  
Healthcare Organizations ◽  
Related Data ◽  
Healthcare Data ◽  
Privacy Violation

Recently, digital health solutions are taking advantage of recent advances in information and communication technologies. In this context, patients' health data are shared with other stakeholders. Moreover, it's now easier to collect massive health data due to the rising use of connected sensors in the health sector. However, the sensitivity of this shared healthcare data related to patients may increase the risks of privacy violation. Therefore, healthcare-related data need robust security measurements to prevent its disclosure and preserve patients' privacy. However, in order to make well-informed decisions, it is often necessary to allow more permissive security policies for healthcare organizations even without the consent of patients or against their preferences. The authors of this chapter concentrate on highlighting these challenging issues related to patient privacy and presenting some of the most significant privacy preserving approaches in the context of digital health.

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.

scholarly journals Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marta Bogowicz ◽  
Arthur Jochems ◽  
Timo M. Deist ◽  
Stephanie Tanadini-Lang ◽  
Shao Hui Huang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Distributed Learning ◽  
Privacy Preserving ◽  
Hpv Status

scholarly journals Privacy Preservation of Healthcare Data in Hybrid Cloud using a Hybrid Meta-Heuristics Based Sanitization Technique

2019 ◽  
Vol 8 (4) ◽  
pp. 2882-2890
Keyword(s):  
Side Effects ◽  
Search Algorithm ◽  
Cloud Model ◽  
Gravitational Search Algorithm ◽  
Data Access ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Sensitive Information ◽  
Hybrid Cloud ◽  
Healthcare Data

Over the recent years, the expansion of cloud computing services enable hospitals and institutions to transit their healthcare data to the cloud, thus it provides the worldwide data access and on-demand high quality services at a cheaper rate. Despite the benefits of healthcare cloud services, the associated privacy issues are widely concerned by individuals and governments. Privacy risks rise when outsourcing personal healthcare records to cloud due to the sensitive nature of health information and the social and legal implications for its disclosure. Over the recent years, a privacy-preserving data mining (PPDM) technique has become a critical issue for the problems. Our goal is to design a privacy-preserving outsourcing framework under the hybrid cloud model. In this work we propose a Hybrid Ant Colony Optimization and Gravitational Search Algorithm (ACOGSA) to express the problem of hiding sensitive data through transaction deletion. Thus, it reduces the side effects of the hybrid cloud. Substantive experiments will be carried to compare the performance of the designed algorithm with the state-of-the-art approaches in terms of the side effects and database similarity (integrity). Over the past to sanitize the databases used for hiding sensitive information, a few heuristic approaches have been proposed. The method used for the comparison involves GA, PSO, ACO, and Firefly framework.

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