Chaotic geometric data perturbed and ensemble gradient homomorphic privacy preservation over big healthcare data

2021 ◽  
Author(s):  
K. Sujatha ◽  
V. Udayarani
Keyword(s):  
Privacy Preservation ◽  
Healthcare Data ◽  
Geometric Data

A Secure Three Factor Based Authentication Scheme for Telecare Medicine Information System with Privacy Preservation

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Information System ◽  
Privacy Preservation ◽  
Chaotic Map ◽  
Security Analysis ◽  
Security And Privacy ◽  
Telecare Medicine Information System ◽  
Medicine Information ◽  
Sensitive Data ◽  
Healthcare Data ◽  
Remote Healthcare

Telecare Medicine Information System (TMIS) is now attracting field for remote healthcare, diagnosis and emergency health services etc. The major objective of this type of system is to provide medical facilities to patients who are critically ill and unable to attend hospitals or put in isolation for observations. A major challenge of such systems is to securely transmit patients' health related information to the medical server through an insecure channel. This collected sensitive data is further used by medical practitioners for diagnosis and treatment purposes. Therefore, security and privacy are essential for healthcare data. In this paper, a robust authentication protocol based on Chebyshev Chaotic map has been proposed for adequate security while transmitting data. The privacy preservation is maintained by a rule set which mainly controls the views. A detailed security analysis was performed for the proposed scheme.

scholarly journals A Non-Reversible Privacy Preservation Model for Outsourced High-Dimensional Healthcare Data

2021 ◽  
Author(s):  
Syed Usama Khalid Bukhari ◽  
Anum Qureshi ◽  
Adeel Anjum ◽  
Munam Ali Shah
Keyword(s):  
Privacy Preservation ◽  
Differential Privacy ◽  
Personal Information ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Sensitive Data ◽  
Privacy Concerns ◽  
Healthcare Data ◽  
Pros And Cons ◽  
Privacy Breaches

<div> <div> <div> <p>Privacy preservation of high-dimensional healthcare data is an emerging problem. Privacy breaches are becoming more common than before and affecting thousands of people. Every individual has sensitive and personal information which needs protection and security. Uploading and storing data directly to the cloud without taking any precautions can lead to serious privacy breaches. It’s a serious struggle to publish a large amount of sensitive data while minimizing privacy concerns. This leads us to make crucial decisions for the privacy of outsourced high-dimensional healthcare data. Many types of privacy preservation techniques have been presented to secure high-dimensional data while keeping its utility and privacy at the same time but every technique has its pros and cons. In this paper, a novel privacy preservation NRPP model for high-dimensional data is proposed. The model uses a privacy-preserving generative technique for releasing sensitive data, which is deferentially private. The contribution of this paper is twofold. First, a state-of-the-art anonymization model for high-dimensional healthcare data is proposed using a generative technique. Second, achieved privacy is evaluated using the concept of differential privacy. The experiment shows that the proposed model performs better in terms of utility. </p> </div> </div> </div>

scholarly journals A Non-Reversible Privacy Preservation Model for Outsourced High-Dimensional Healthcare Data

2021 ◽  
Author(s):  
Syed Usama Khalid Bukhari ◽  
Anum Qureshi ◽  
Adeel Anjum ◽  
Munam Ali Shah
Keyword(s):  
Privacy Preservation ◽  
Differential Privacy ◽  
Personal Information ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Sensitive Data ◽  
Privacy Concerns ◽  
Healthcare Data ◽  
Pros And Cons ◽  
Privacy Breaches

<div> <div> <div> <p>Privacy preservation of high-dimensional healthcare data is an emerging problem. Privacy breaches are becoming more common than before and affecting thousands of people. Every individual has sensitive and personal information which needs protection and security. Uploading and storing data directly to the cloud without taking any precautions can lead to serious privacy breaches. It’s a serious struggle to publish a large amount of sensitive data while minimizing privacy concerns. This leads us to make crucial decisions for the privacy of outsourced high-dimensional healthcare data. Many types of privacy preservation techniques have been presented to secure high-dimensional data while keeping its utility and privacy at the same time but every technique has its pros and cons. In this paper, a novel privacy preservation NRPP model for high-dimensional data is proposed. The model uses a privacy-preserving generative technique for releasing sensitive data, which is deferentially private. The contribution of this paper is twofold. First, a state-of-the-art anonymization model for high-dimensional healthcare data is proposed using a generative technique. Second, achieved privacy is evaluated using the concept of differential privacy. The experiment shows that the proposed model performs better in terms of utility. </p> </div> </div> </div>

scholarly journals A Hyperledger Fabric-Based System Framework for Healthcare Data Management

2021 ◽  
Vol 11 (24) ◽  
pp. 11693
Author(s):  
Qianyu Wang ◽  
Shaowen Qin
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Privacy Preserving ◽  
Smart Contracts ◽  
Medical Practices ◽  
Healthcare Data ◽  
Blockchain Technology ◽  
Existing Data

This study examined the requirements for privacy-preserving and interoperability in healthcare data sharing and proposed a blockchain-based solution. The Hyperledger Fabric framework was adopted due to its enterprise-grade data processing capabilities and enhanced privacy protection functions. In addition to the Fabric’s built-in privacy-preserving functions, healthcare data-specific smart contracts with hierarchical access control were developed to strengthen privacy protection in data sharing. The proposed healthcare data-sharing framework is based on Australian medical practices with the aim to upgrade, rather than to replace, the existing data management models. The outcome of this study demonstrates the feasibility of applying blockchain technology to improve privacy-preservation while enhancing interoperability in healthcare data management.

A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology

2021 ◽  
Author(s):  
Saurabh Singh ◽  
Shailendra Rathore ◽  
Osama Alfarraj ◽  
Amr Tolba ◽  
Byungun Yoon
Keyword(s):  
Privacy Preservation ◽  
Healthcare Data ◽  
Blockchain Technology

scholarly journals Permissioned Healthcare Blockchain System for Securing the EHRs with Privacy Preservation

2021 ◽  
Vol 26 (4) ◽  
pp. 393-402
Author(s):  
Katru Rama Rao ◽  
Satuluri Naganjaneyulu
Keyword(s):  
Privacy Preservation ◽  
Single Point ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Electronic Healthcare Record ◽  
Proposed Model ◽  
Authentication And Authorization ◽  
Health Institutions ◽  
Central Database

Healthcare data is very sensitive as many healthcare organizations will be very reluctant to share health data. However, sharing the healthcare data is having many more uses for both the patients as well as the research institutions too. Moreover, the existing Electronic Healthcare Record (EHR) management system will be stored in the central database in the form of plaintext. Whenever the data needs to be accessed from the database, the users will be requesting the required EHRs. However, this mechanism possesses the several challenges such as single point of failure, takes more time for user identification, interoperability issues, data recoverability issues, lack of privacy and security. This paper mainly focuses on providing security for the healthcare data, which can be shared among the various health institutions. Authentication and authorization are provided by establishing multiple certification authorities on the permissioned healthcare blockchain network. In this proposed model data integrity is also achieved by the concept of hashing of the electronic health records rather than storing it directly onto the permissioned healthcare block chain network.

scholarly journals PRIVACY PRESERVING CLUSTERING BASED ON SINGULAR VALUE DECOMPOSITION AND GEOMETRIC DATA PERTURBATION

2013 ◽  
Vol 10 (3) ◽  
pp. 1427-1433 ◽  
Author(s):  
M. Naga Lakshmi ◽  
Dr. K Sandhya Rani
Keyword(s):  
Data Mining ◽  
Singular Value Decomposition ◽  
Hybrid Method ◽  
Privacy Preservation ◽  
Hybrid Methods ◽  
Singular Value ◽  
Data Perturbation ◽  
Geometric Data ◽  
Reflection Data ◽  
Value Decomposition

Privacy preservation is a major concern when the application of data mining techniques to large repositories of data consists of personal, sensitive and confidential information. Singular Value Decomposition (SVD) is a matrix factorization method, which can produces perturbed data by efficiently removing unnecessary information for data mining. In this paper two hybrid methods are proposed which takes the advantage of existing techniques SVD and geometric data transformations in order to provide better privacy preservation. Reflection data perturbation and scaling data perturbation are familiar geometric data transformation methods which retains the statistical properties in the dataset. In hybrid method one, SVD and scaling data perturbation are used as a combination to obtain the distorted dataset. In hybrid method two, SVD and reflection data perturbation methods are used as a combination to obtain the distorted dataset. The experimental results demonstrated that the proposed hybrid methods are providing higher utility without breaching privacy.

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