scholarly journals P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification

2019 ◽  
Author(s):  
Bingzhe Wu ◽  
Shiwan Zhao ◽  
Guangyu Sun ◽  
Xiaolu Zhang ◽  
Zhong Su ◽  
...  
Keyword(s):  
Image Classification ◽  
Privacy Preserving ◽  
Patient Privacy ◽  
Pathological Image

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.

scholarly journals Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

2019 ◽  
Vol 1 (1) ◽  
pp. 483-491 ◽  
Author(s):  
Makhamisa Senekane
Keyword(s):  
Support Vector Machine ◽  
Data Analysis ◽  
Image Classification ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Global Optimum ◽  
Support Vector ◽  
Sensitive Data ◽  
Radiological Images ◽  
Golden Standard

The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.

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 Multi-Stage Pathological Image Classification Using Semantic Segmentation

2019 ◽  
Author(s):  
Shusuke Takahama ◽  
Yusuke Kurose ◽  
Yusuke Mukuta ◽  
Hiroyuki Abe ◽  
Masashi Fukayama ◽  
...  
Keyword(s):  
Image Classification ◽  
Semantic Segmentation ◽  
Multi Stage ◽  
Pathological Image

scholarly journals Privacy preserving-probabilistic record linkage to assess cancer outcomes in people living with HIV in South Africa

2021 ◽  
Author(s):  
Julia Bohlius ◽  
Lina Bartels ◽  
Frédérique Chammartin ◽  
Victor Olago ◽  
Adrian Spoerri ◽  
...  
Keyword(s):  
South Africa ◽  
Record Linkage ◽  
Cancer Control ◽  
Population Based ◽  
Privacy Preserving ◽  
People Living With Hiv ◽  
Patient Privacy ◽  
Cancer Outcomes ◽  
Probabilistic Record Linkage ◽  
Living With Hiv

Background: Privacy-preserving probabilistic record linkage (PPPRL) methods were developed and applied in high-income countries to link records within and between organizations under strict privacy protections. PPPRL has not yet been used in African settings.Methods: We used HIV-related laboratory records from National Health Laboratory Services (NHLS) in South Africa to construct a cohort of HIV-positive patients and link them to the National Cancer Registry (NCR) with PPPRL. The study was restricted to Gauteng province from 2004 to 2014. We used records with national IDs (gold standard) to determine precision, recall, and f-measure of the linkages. We included all patients with ≥ 2 HIV-related lab records measured in the cohort and assessed the number of cancers diagnosed in people living with HIV (PLWH).Results: We included 11,480,118 HIV-related laboratory records and 664,869 cancer records in the linkage. We included 1,173,908 persons in the HIV cohort; 66.6% were female and median age at first HIV-related lab test was 33.9 years (IQR 27.4-41.3). Of the patients in the cohort, 26,348 were diagnosed with at least one cancer and 8,329 of these cancers were diagnosed before or on the date of the patient’s first HIV-related record; 18,019 were diagnosed after their first HIV-related record. For all linkages, precision, recall, and f-measures were high.Conclusion: Our study showed it is feasible to use PPPRL in an African setting to link routinely collected health records from different data sources and create a longitudinal HIV cohort with cancer outcomes while strictly protecting patient privacy. This work served as the foundation to create a nationwide population-based cohort including all South African provinces which will be used to inform cancer control programs.

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