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.

scholarly journals The issues connected with the anonymization of medical data. Part 2. Advanced anonymization and anonymization controlled by owner of protected sensitive data

2014 ◽  
Vol 8 (2) ◽  
pp. 13-24 ◽  
Author(s):  
Arkadiusz Liber
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Data Access ◽  
Medical Data ◽  
Medical Documentation ◽  
Research Review ◽  
Sensitive Data ◽  
Data Anonymization ◽  
Data Access Control ◽  
Anonymized Data

Introduction: Medical documentation ought to be accessible with the preservation of its integrity as well as the protection of personal data. One of the manners of its protection against disclosure is anonymization. Contemporary methods ensure anonymity without the possibility of sensitive data access control. it seems that the future of sensitive data processing systems belongs to the personalized method. In the first part of the paper k-Anonymity, (X,y)- Anonymity, (α,k)- Anonymity, and (k,e)-Anonymity methods were discussed. these methods belong to well - known elementary methods which are the subject of a significant number of publications. As the source papers to this part, Samarati, Sweeney, wang, wong and zhang’s works were accredited. the selection of these publications is justified by their wider research review work led, for instance, by Fung, Wang, Fu and y. however, it should be noted that the methods of anonymization derive from the methods of statistical databases protection from the 70s of 20th century. Due to the interrelated content and literature references the first and the second part of this article constitute the integral whole.Aim of the study: The analysis of the methods of anonymization, the analysis of the methods of protection of anonymized data, the study of a new security type of privacy enabling device to control disclosing sensitive data by the entity which this data concerns.Material and methods: Analytical methods, algebraic methods.Results: Delivering material supporting the choice and analysis of the ways of anonymization of medical data, developing a new privacy protection solution enabling the control of sensitive data by entities which this data concerns.Conclusions: In the paper the analysis of solutions for data anonymization, to ensure privacy protection in medical data sets, was conducted. the methods of: k-Anonymity, (X,y)- Anonymity, (α,k)- Anonymity, (k,e)-Anonymity, (X,y)-Privacy, lKc-Privacy, l-Diversity, (X,y)-linkability, t-closeness, confidence Bounding and Personalized Privacy were described, explained and analyzed. The analysis of solutions of controlling sensitive data by their owner was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the protection of anonymized data was included. In particular, the methods of: δ-Presence, e-Differential Privacy, (d,γ)-Privacy, (α,β)-Distributing Privacy and protections against (c,t)-isolation were analyzed. Moreover, the author introduced a new solution of the controlled protection of privacy. the solution is based on marking a protected field and the multi-key encryption of sensitive value. The suggested way of marking the fields is in accordance with Xmlstandard. For the encryption, (n,p) different keys cipher was selected. to decipher the content the p keys of n were used. The proposed solution enables to apply brand new methods to control privacy of disclosing sensitive data.

scholarly journals A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study

Inventions ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 45
Author(s):  
Prathamesh Churi ◽  
Ambika Pawar ◽  
Antonio-José Moreno-Guerrero
Keyword(s):  
Healthcare System ◽  
Large Population ◽  
Healthcare Systems ◽  
Healthcare Management ◽  
Healthcare Sector ◽  
Sensitive Data ◽  
Data Breaches ◽  
Healthcare Data ◽  
Privacy Issues

Background: According to the renowned and Oscar award-winning American actor and film director Marlon Brando, “privacy is not something that I am merely entitled to, it is an absolute prerequisite.” Privacy threats and data breaches occur daily, and countries are mitigating the consequences caused by privacy and data breaches. The Indian healthcare industry is one of the largest and rapidly developing industry. Overall, healthcare management is changing from disease-centric into patient-centric systems. Healthcare data analysis also plays a crucial role in healthcare management, and the privacy of patient records must receive equal attention. Purpose: This paper mainly presents the utility and privacy factors of the Indian healthcare data and discusses the utility aspect and privacy problems concerning Indian healthcare systems. It defines policies that reform Indian healthcare systems. The case study of the NITI Aayog report is presented to explain how reformation occurs in Indian healthcare systems. Findings: It is found that there have been numerous research studies conducted on Indian healthcare data across all dimensions; however, privacy problems in healthcare, specifically in India, are caused by prevalent complacency, culture, politics, budget limitations, large population, and existing infrastructures. This paper reviews the Indian healthcare system and the applications that drive it. Additionally, the paper also maps that how privacy issues are happening in every healthcare sector in India. Originality/Value: To understand these factors and gain insights, understanding Indian healthcare systems first is crucial. To the best of our knowledge, we found no recent papers that thoroughly reviewed the Indian healthcare system and its privacy issues. The paper is original in terms of its overview of the healthcare system and privacy issues. Social Implications: Privacy has been the most ignored part of the Indian healthcare system. With India being a country with a population of 130 billion, much healthcare data are generated every day. The chances of data breaches and other privacy violations on such sensitive data cannot be avoided as they cause severe concerns for individuals. This paper segregates the healthcare system’s advances and lists the privacy that needs to be addressed first.

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 The issues connected with the anonymization of medical data. Part 1. The introduction to the anonymization of medical data. Ensuring the protection of sensitive information with the use of such methods as f(a) and f(a,b)

2014 ◽  
Vol 8 (1) ◽  
pp. 13-21 ◽  
Author(s):  
ARKADIUSZ LIBER
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Differential Privacy ◽  
Medical Data ◽  
Medical Documentation ◽  
Sensitive Information ◽  
Sensitive Data ◽  
New Methods ◽  
Anonymized Data ◽  
Data Privacy Protection

Introduction: Medical documentation must be protected against damage or loss, in compliance with its integrity and credibility and the opportunity to a permanent access by the authorized staff and, finally, protected against the access of unauthorized persons. Anonymization is one of the methods to safeguard the data against the disclosure.Aim of the study: The study aims at the analysis of methods of anonymization, the analysis of methods of the protection of anonymized data and the study of a new security type of privacy enabling to control sensitive data by the entity which the data concerns.Material and methods: The analytical and algebraic methods were used.Results: The study ought to deliver the materials supporting the choice and analysis of the ways of the anonymization of medical data, and develop a new privacy protection solution enabling the control of sensitive data by entities whom this data concerns.Conclusions: In the paper, the analysis of solutions of data anonymizing used for medical data privacy protection was con-ducted. The methods, such as k-Anonymity, (X,y)- Anonymity, (a,k)- Anonymity, (k,e)-Anonymity, (X,y)-Privacy, LKC-Privacy, l-Diversity, (X,y)-Linkability, t-Closeness, Confidence Bounding and Personalized Privacy were described, explained and analyzed. The analysis of solutions to control sensitive data by their owners was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the anonimized data protection was conducted, in particular the methods of: d-Presence, e-Differential Privacy, (d,g)-Privacy, (a,b)-Distributing Privacy and protections against (c,t)-Isolation were analyzed. The author introduced a new solution of the controlled protection of privacy. The solution is based on marking a protected field and multi-key encryption of the sensitive value. The suggested way of fields marking is in accordance to the XML standard. For the encryption (n,p) different key cipher was selected. To decipher the content the p keys of n is used. The proposed solution enables to apply brand new methods for the control of privacy of disclosing sensitive data.

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Abhinav Kumar ◽  
Sanjay Kumar Singh ◽  
K Lakshmanan ◽  
Sonal Saxena ◽  
Sameer Shrivastava
Keyword(s):  
Differential Privacy ◽  
Model Performance ◽  
Healthcare Services ◽  
Cloud Services ◽  
Efficient Computation ◽  
Sensitive Data ◽  
Classification Framework ◽  
Proposed Model ◽  
Diagnostic Framework ◽  
Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

2013 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Pingfeng Wang ◽  
Chao Hu
Keyword(s):  
Sampling Method ◽  
Failure Time ◽  
Health Management ◽  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Statistical Relationship ◽  
Cooling Fans ◽  
On Line

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

scholarly journals Developing learning diaries for action research on healthcare management in Ghana, Tanzania and Uganda

2016 ◽  
Vol 14 (4) ◽  
pp. 412-434 ◽  
Author(s):  
Comfort Mshelia ◽  
Gillian Lê ◽  
Tolib Mirzoev ◽  
Samuel Amon ◽  
Ambrose Kessy ◽  
...  
Keyword(s):  
Action Research ◽  
Health Management ◽  
Healthcare Management ◽  
Sub Saharan Africa ◽  
District Health ◽  
The People ◽  
Effective Form ◽  
Recording Process ◽  
Specific Practice ◽  
Sub Saharan

Action research (AR) can be an effective form of ‘on the job’ training. However, it is critical that AR cycles can be appropriately recorded in order to contribute to reflection and learning. One form of recording is for coresearchers to keep a diary. We found no previous literature describing the use of diaries in AR in sub-Saharan Africa. We therefore use this paper to reflect on how diaries were used by district health management teams in the PERFORM project. We share five lessons from our experience. First, it is important to foster ownership of the diary by the people who are responsible for filling it in. Second, the purpose of keeping a diary needs to be clear and shared between researchers and practitioners from the very beginning. Third, diaries should be allowed to evolve. Fourth, it is a challenge for busy practitioners to record the reflection and learning processes that they go through. Last, diaries on their own are not sufficient to capture reflection and learning. In conclusion, there is no best way for practitioners to keep a diary; rather the focus should be on ensuring that an AR recording process (whether diary or otherwise) is locally owned and complements the specific practice setting.

Sign in / Sign up

Export Citation Format

Share Document