Privacy protection for clinical and genomic data

Claerhout, Brecht; DeMoor, G. J. E.

doi:10.1016/j.ijmedinf.2004.03.008

Cited by 37 publications

(4 citation statements)

References 8 publications

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“…In light of recent work suggesting that people prefer to withhold information from medical professionals in the presence of security concerns [ 34 ], it is essential to identify ways to ensure both meaningful use of health information and trustworthy sharing practices that safeguard patient privacy. Priorities for further exploration and practical application might include: privacy by design principles that incorporate privacy protection in the design stage, rather than viewing it as an add-on requirement [ 57 ], privacy enhancing techniques [ 58 ], differential mechanisms that enhance de-identification in database searches [ 59 , 60 ], dynamic context-aware policies [ 61 ], purpose-based policies [ 62 ] and notification of privacy breaches to data subjects [ 63 ]. Patient control and choice could also be increased through the use of dynamic informed consent and revocation options, formally allowing nuanced preference management as part of EHR systems [ 64 ].…”

Section: Discussionmentioning

confidence: 99%

Patient and public views about the security and privacy of Electronic Health Records (EHRs) in the UK: results from a mixed methods study

Papoutsi

Reed

Marston

et al. 2015

BMC Med Inform Decis Mak

117

148

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BackgroundAlthough policy discourses frame integrated Electronic Health Records (EHRs) as essential for contemporary healthcare systems, increased information sharing often raises concerns among patients and the public. This paper examines patient and public views about the security and privacy of EHRs used for health provision, research and policy in the UK.MethodsSequential mixed methods study with a cross-sectional survey (in 2011) followed by focus group discussions (in 2012-2013). Survey participants (N = 5331) were recruited from primary and secondary care settings in West London (UK). Complete data for 2761 (51.8 %) participants were included in the final analysis for this paper. The survey results were discussed in 13 focus groups with people living with a range of different health conditions, and in 4 mixed focus groups with patients, health professionals and researchers (total N = 120). Qualitative data were analysed thematically.ResultsIn the survey, 79 % of participants reported that they would worry about the security of their record if this was part of a national EHR system and 71 % thought the National Health Service (NHS) was unable to guarantee EHR safety at the time this work was carried out. Almost half (47 %) responded that EHRs would be less secure compared with the way their health record was held at the time of the survey. Of those who reported being worried about EHR security, many would nevertheless support their development (55 %), while 12 % would not support national EHRs and a sizeable proportion (33 %) were undecided. There were also variations by age, ethnicity and education. In focus group discussions participants weighed up perceived benefits against potential security and privacy threats from wider sharing of information, as well as discussing other perceived risks: commercial exploitation, lack of accountability, data inaccuracies, prejudice and inequalities in health provision.ConclusionsPatient and public worries about the security risks associated with integrated EHRs highlight the need for intensive public awareness and engagement initiatives, together with the establishment of trustworthy security and privacy mechanisms for health information sharing.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0202-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Patient and public views about the security and privacy of Electronic Health Records (EHRs) in the UK: results from a mixed methods study

Papoutsi

Reed

Marston

et al. 2015

BMC Med Inform Decis Mak

117

148

View full text Add to dashboard Cite

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“…is approach relies on generators to generate high-quality training data points; therefore, it performs poorly in complex datasets [6]. Phan et al combined a deep encoder with various privacy techniques, adding a global sensitivity computing layer to the encoder based on a gradient descent approach to provide optimal perturbation parameters and then fine-tuning model parameters using a back-propagation algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection Technology Based on Machine Learning and Intelligent Data Recognition

liu¹

2022

Security and Communication Networks

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The machine learning algorithm is gradually being applied to various fields and has become the core technology to achieve artificial intelligence. The success of machine learning cannot be achieved without the support of large amounts of data and computing power, which are usually collected through crowdsourcing and learned online. The data collected for machine learning training often contains some personal and sensitive information, including personal mobile phone numbers, ID numbers, and medical information. How to protect these private data at low cost and efficiently is an important problem. Aiming at this kind of problem, this article starts with the privacy problem in machine learning and the way of being attacked and summarizes the privacy protection methods and characteristics in the machine learning algorithm. Then, for the classification accuracy of the different algorithm that uses noise to protect privacy, a deep difference privacy protection method combined with a convolutional neural network is proposed. This method perfectly integrates the features of difference and Gaussian distribution and can obtain the privacy budget of each layer of the neural network. Finally, the stochastic gradient descent algorithm's gradient value is employed to set the Gaussian noise scale and preserve the data's sensitive information. The experimental results demonstrated that by adjusting the parameters of the depth differential privacy model based on differences in private information in the data, a balance between the availability and privacy protection of the training data set could be reached.

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“…Data security and patient confidentiality is fundamental to this study. Stringent privacy protections guiding the release and use of the data are in place using pseudonymization (use of pseudo-identities to replace true individuals' identities) [63][64]. Only de-identified data are requested for this study.…”

Section: Resultsmentioning

confidence: 99%

BIG DATA DRIVEN CLINICAL INFORMATICS & SURVEILLANCE (BDD_CIS) - A MULTIMODAL DATABASE FOCUSED CLINICAL, COMMUNITY, AND MULTI-OMICS SURVEILLANCE PLAN FOR COVID-19: A STUDY PROTOCOL (Preprint)

Olatosi¹,

Zhang²,

Weissman³

et al. 2020

Preprint

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BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

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Privacy protection for clinical and genomic data

Cited by 37 publications

References 8 publications

Patient and public views about the security and privacy of Electronic Health Records (EHRs) in the UK: results from a mixed methods study

Patient and public views about the security and privacy of Electronic Health Records (EHRs) in the UK: results from a mixed methods study

Privacy Protection Technology Based on Machine Learning and Intelligent Data Recognition

BIG DATA DRIVEN CLINICAL INFORMATICS & SURVEILLANCE (BDD_CIS) - A MULTIMODAL DATABASE FOCUSED CLINICAL, COMMUNITY, AND MULTI-OMICS SURVEILLANCE PLAN FOR COVID-19: A STUDY PROTOCOL (Preprint)

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