Interactive Range Queries for Healthcare Data under Differential Privacy

2021 ◽  
Author(s):  
Asma Alnemari ◽  
Rajendra K. Raj ◽  
Carol J. Romanowski ◽  
Sumita Mishra
Keyword(s):  
Differential Privacy ◽  
Range Queries ◽  
Healthcare Data

scholarly journals Answering range queries under local differential privacy

2019 ◽  
Vol 12 (10) ◽  
pp. 1126-1138 ◽  
Author(s):  
Graham Cormode ◽  
Tejas Kulkarni ◽  
Divesh Srivastava
Keyword(s):  
Differential Privacy ◽  
Range Queries

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 Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3019
Author(s):  
Young-Hoon Park ◽  
Yejin Kim ◽  
Junho Shim
Keyword(s):  
Data Management ◽  
Differential Privacy ◽  
Genomic Data ◽  
Third Party ◽  
Data Management System ◽  
Sensitive Information ◽  
Healthcare Data ◽  
Large Size ◽  
Gene Data ◽  
And Control

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

scholarly journals Privacy-preserving healthcare informatics: a review

2021 ◽  
Vol 36 ◽  
pp. 04005
Author(s):  
Kah Meng Chong
Keyword(s):  
Data Privacy ◽  
Differential Privacy ◽  
State Of The Art ◽  
Future Research ◽  
Sensitive Information ◽  
Research Directions ◽  
Data Anonymization ◽  
Healthcare Data ◽  
Comprehensive Survey ◽  
Future Research Directions

Electronic Health Record (EHR) is the key to an efficient healthcare service delivery system. The publication of healthcare data is highly beneficial to healthcare industries and government institutions to support a variety of medical and census research. However, healthcare data contains sensitive information of patients and the publication of such data could lead to unintended privacy disclosures. In this paper, we present a comprehensive survey of the state-of-the-art privacy-enhancing methods that ensure a secure healthcare data sharing environment. We focus on the recently proposed schemes based on data anonymization and differential privacy approaches in the protection of healthcare data privacy. We highlight the strengths and limitations of the two approaches and discussed some promising future research directions in this area.

scholarly journals Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2733 ◽  
Author(s):  
Arijit Ukil ◽  
Antonio J. Jara ◽  
Leandro Marin
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Health Management ◽  
Practical Importance ◽  
Healthcare Management ◽  
Machine Intelligence ◽  
Data Driven ◽  
Sensitive Data ◽  
Cardiac Health ◽  
Healthcare Data

Remote and automated healthcare management has shown the prospective to significantly impact the future of human prognosis rate. Internet of Things (IoT) enables the development and implementation ecosystem to cater the need of large number of relevant stakeholders. In this paper, we consider the cardiac health management system to demonstrate that data-driven techniques produce substantial performance merits in terms of clinical efficacy by employing robust machine learning methods with relevant and selected signal processing features. We consider phonocardiogram (PCG) or heart sound as the exemplary physiological signal. PCG carries substantial cardiac health signature to establish our claim of data-centric superior clinical utility. Our method demonstrates close to 85% accuracy on publicly available MIT-Physionet PCG datasets and outperform relevant state-of-the-art algorithm. Due to its simpler computational architecture of shallow classifier with just three features, the proposed analytics method is performed at edge gateway. However, it is to be noted that healthcare analytics deal with number of sensitive data and subsequent inferences, which need privacy protection. Additionally, the problem of healthcare data privacy prevention is addressed by de-risking of sensitive data management using differential privacy, such that controlled privacy protection on sensitive healthcare data can be enabled. When a user sets for privacy protection, appropriate privacy preservation is guaranteed for defense against privacy-breaching knowledge mining attacks. In this era of IoT and machine intelligence, this work is of practical importance, which enables on-demand automated screening of cardiac health under minimizing the privacy breaching risk.

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