scholarly journals Protecting patient privacy in survival analyses

2019 ◽  
Vol 27 (3) ◽  
pp. 366-375
Author(s):  
Luca Bonomi ◽  
Xiaoqian Jiang ◽  
Lucila Ohno-Machado
Keyword(s):  
Survival Analysis ◽  
Biomedical Research ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Future Research ◽  
Patient Privacy ◽  
Privacy Risk ◽  
Survival Curves ◽  
Healthcare Applications ◽  
Survival Analyses

Abstract Objective Survival analysis is the cornerstone of many healthcare applications in which the “survival” probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis. Materials and Methods We develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model. Results We empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset. Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves. Discussion The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications. Conclusion The results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.

scholarly journals A Survey on Differential Privacy for Unstructured Data Content

2022 ◽  
Author(s):  
Ying Zhao ◽  
Jinjun Chen
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Personal Information ◽  
Unstructured Data ◽  
Future Research ◽  
Huge Amount ◽  
Protection Technology ◽  
Data Content ◽  
Human Faces ◽  
Privacy Models

Huge amount of unstructured data including image, video, audio, and text are ubiquitously generated and shared, it is a challenge to protect sensitive personal information in them, such as human faces, voiceprints, and authorships. Differential privacy is the standard privacy protection technology that provides rigorous privacy guarantees for various data. This survey summarizes and analyzes differential privacy solutions to protect unstructured data content before they are shared with untrusted parties. These differential privacy methods obfuscate unstructured data after they are represented with vectors, and then reconstruct them with obfuscated vectors. We summarize specific privacy models and mechanisms together with possible challenges in them. We also conclude their privacy guarantees against AI attacks and utility losses. Finally, we discuss several possible directions for future research.

scholarly journals A Review of Differential Privacy in Individual Data Release

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Jun Wang ◽  
Shubo Liu ◽  
Yongkai Li
Keyword(s):  
Pattern Mining ◽  
Medical Information ◽  
Differential Privacy ◽  
Rapid Development ◽  
Frequent Pattern ◽  
Future Research ◽  
Healthcare Applications ◽  
Tree Structures ◽  
Research Results ◽  
Data Release

The rapid development of mobile technology has improved users’ quality of treatment, and tremendous amounts of medical information are readily available and widely used in data analysis and application, which bring on serious threats to users’ privacy. Classical methods based on cryptography and anonymous-series models fail due to their high complexity, poor controllability, and dependence on the background knowledge of adversaries when it comes to current mobile healthcare applications. Differential privacy is a relatively new notion of privacy and has become the de facto standard for a security-controlled privacy guarantee. In this paper, the key aspects of basic concepts and implementation mechanisms related to differential privacy are explained, and the existing research results are concluded. The research results presented include methods based on histograms, tree structures, time series, graphs, and frequent pattern mining data release methods. Finally, shortcomings of existing methods and suggested directions for future research are presented.

scholarly journals Using mobile location data in biomedical research while preserving privacy

2018 ◽  
Vol 25 (10) ◽  
pp. 1402-1406 ◽  
Author(s):  
Daniel M Goldenholz ◽  
Shira R Goldenholz ◽  
Kaarkuzhali B Krishnamurthy ◽  
John Halamka ◽  
Barbara Karp ◽  
...  
Keyword(s):  
Biomedical Research ◽  
Privacy Protection ◽  
Institutional Review Boards ◽  
Patient Privacy ◽  
Mobile Location ◽  
Location Data ◽  
Journal Editors ◽  
Informed Decisions ◽  
Institutional Review ◽  
Review Boards

Abstract Location data are becoming easier to obtain and are now bundled with other metadata in a variety of biomedical research applications. At the same time, the level of sophistication required to protect patient privacy is also increasing. In this article, we provide guidance for institutional review boards (IRBs) to make informed decisions about privacy protections in protocols involving location data. We provide an overview of some of the major categories of technical algorithms and medical–legal tools at the disposal of investigators, as well as the shortcomings of each. Although there is no “one size fits all” approach to privacy protection, this article attempts to describe a set of practical considerations that can be used by investigators, journal editors, and IRBs.

scholarly journals A systematic review of the quality of conduct and reporting of survival analyses of tuberculosis outcomes in Africa

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Moses M. Ngari ◽  
Susanne Schmitz ◽  
Christopher Maronga ◽  
Lazarus K. Mramba ◽  
Michel Vaillant
Keyword(s):  
Systematic Review ◽  
Sample Size ◽  
Survival Function ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Survival Curves ◽  
Survival Analyses ◽  
Survival Regression

Abstract Background Survival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context. Methods Systematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria. Results Seventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively. Conclusion The quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

scholarly journals Comparison of blockchain platforms: a systematic review and healthcare examples

2019 ◽  
Vol 26 (5) ◽  
pp. 462-478 ◽  
Author(s):  
Tsung-Ting Kuo ◽  
Hugo Zavaleta Rojas ◽  
Lucila Ohno-Machado
Keyword(s):  
Systematic Review ◽  
Biomedical Research ◽  
Health Informatics ◽  
Clinical Informatics ◽  
Healthcare Applications ◽  
Software Engineers ◽  
Key Features ◽  
Technical Features ◽  
Meta Analyses ◽  
Selection Of

Abstract Objectives To introduce healthcare or biomedical blockchain applications and their underlying blockchain platforms, compare popular blockchain platforms using a systematic review method, and provide a reference for selection of a suitable blockchain platform given requirements and technical features that are common in healthcare and biomedical research applications. Target audience Healthcare or clinical informatics researchers and software engineers who would like to learn about the important technical features of different blockchain platforms to design and implement blockchain-based health informatics applications. Scope Covered topics include (1) a brief introduction to healthcare or biomedical blockchain applications and the benefits to adopt blockchain; (2) a description of key features of underlying blockchain platforms in healthcare applications; (3) development of a method for systematic review of technology, based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, to investigate blockchain platforms for healthcare and medicine applications; (4) a review of 21 healthcare-related technical features of 10 popular blockchain platforms; and (5) a discussion of findings and limitations of the review.

scholarly journals A Comprehensive Survey on Local Differential Privacy

2020 ◽  
Vol 2020 ◽  
pp. 1-29 ◽  
Author(s):  
Xingxing Xiong ◽  
Shubo Liu ◽  
Dan Li ◽  
Zhaohui Cai ◽  
Xiaoguang Niu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Learning ◽  
Data Privacy ◽  
Statistical Estimation ◽  
Differential Privacy ◽  
Future Research ◽  
Reference Source ◽  
Complex Data ◽  
Comprehensive Survey

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

Impact and paybacks of biomedical research findings in Malaysia 2005–2015

2021 ◽  
Author(s):  
Pei Kuan Lai ◽  
S Nalliah ◽  
CL Teng ◽  
NLP Chen
Keyword(s):  
Decision Making ◽  
Health System ◽  
Capacity Building ◽  
Biomedical Research ◽  
Knowledge Production ◽  
Future Research ◽  
Relevant Research ◽  
Research Findings ◽  
The Impact ◽  
Health System Policy

Background: Impact in research encompasses health, economic, and cultural benefits beyond adding to the knowledge base. Funders are under immense pressure to be accountable for the paybacks from funded research.Aims and objectives: The aim of this study was to look into the impact of funded biomedical research between the years 2005 and 2015 in Malaysia from the aspects of knowledge production, research targeting and capacity building, as well as health system policy and decision making.Methods: This study employed a convergent parallel mixed-methods research design. Biomedical projects related to breast cancer, coronary heart disease, and dengue, funded by the Ministry of Health (MOH), Ministry of Higher Education (MOHE), and Ministry of Science, Technology, and Innovation (MOSTI) between the years 2005 and 2015, were included.Findings: From the questionnaire responses (n=58), on average each funded project managed to produce two outputs and one higher degree student. More than half (61.4%) of the funded projects led to subsequent future research. However, low citations in systematic reviews (10.3%), health policies (6.9%), and clinical practice guidelines (5.2%) were reported. In-depth interviews with the key opinion leaders also saw that most of the local research findings were found to be irrelevant to be adopted into policies by the policymakers.Discussion and conclusions: Paybacks on knowledge production as well as research targeting and capacity building had been achieved, but impact on health system policy and decision making had not been well attained, due to the lack of relevant research findings needed by the policymakers.<br />Key messages<br /><ul><li>Payback on knowledge production was achieved, as there had been a lot of new knowledge generated as captured in academic publications, conference proceedings, policy briefs, technical reports, and research highlights, which is important to advance the frontiers of knowledge.</li><br /><li>Payback on research targeting was achieved, with the current research leading to future study with identification of the knowledge gap and generation of new ideas for new research.</li><br /><li>Payback on capacity building was achieved with the training of researchers, building up research capacity and competencies, production of MSc and PhD graduates, promotion of lecturers, and development of new partnerships and networks.</li><br /><li>Impact on health system policy and decision making was not well attained. There had been a lack of relevant research data and findings being incorporated into policymaking, due to the basic and fundamental nature of most of the funded biomedical research in Malaysia.</li></ul>

scholarly journals Between protection and disclosure: applying the privacy calculus to investigate the intended use of privacy-protecting tools and self-disclosure on different websites

2021 ◽  
Vol 10 (3) ◽  
pp. 283-306
Author(s):  
Yannic Meier ◽  
Johanna Schäwel ◽  
Nicole C. Krämer
Keyword(s):  
Privacy Protection ◽  
Privacy Risk ◽  
Basic Notion ◽  
Self Disclosure ◽  
Privacy Calculus ◽  
Willingness To Use ◽  
Privacy Risks ◽  
Risk And Benefit ◽  
Privacy Behaviors ◽  
Intended Use

Using privacy-protecting tools and reducing self-disclosure can decrease the likelihood of experiencing privacy violations. Whereas previous studies found people’s online self-disclosure being the result of privacy risk and benefit perceptions, the present study extended this so-called privacy calculus approach by additionally focusing on privacy protection by means of a tool. Furthermore, it is important to understand contextual differences in privacy behaviors as well as characteristics of privacy-protecting tools that may affect usage intention. Results of an online experiment (N = 511) supported the basic notion of the privacy calculus and revealed that perceived privacy risks were strongly related to participants’ desired privacy protection which, in turn, was positively related to the willingness to use a privacy-protecting tool. Self-disclosure was found to be context dependent, whereas privacy protection was not. Moreover, participants would rather forgo using a tool that records their data, although this was described to enhance privacy protection.

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