scholarly journals Synthetic data use: exploring use cases to optimise data utility

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Stefanie James ◽  
Chris Harbron ◽  
Janice Branson ◽  
Mimmi Sundler
Keyword(s):  
Pharmaceutical Industry ◽  
Data Privacy ◽  
Synthetic Data ◽  
Use Cases ◽  
Data Utility ◽  
Original Dataset ◽  
Simulation Based ◽  
Synthetic Datasets ◽  
Future Direction

AbstractSynthetic data is a rapidly evolving field with growing interest from multiple industry stakeholders and European bodies. In particular, the pharmaceutical industry is starting to realise the value of synthetic data which is being utilised more prevalently as a method to optimise data utility and sharing, ultimately as an innovative response to the growing demand for improved privacy. Synthetic data is data generated by simulation, based upon and mirroring properties of an original dataset. Here, with supporting viewpoints from across the pharmaceutical industry, we set out to explore use cases for synthetic data across seven key but relatable areas for optimising data utility for improved data privacy and protection. We also discuss the various methods which can be used to produce a synthetic dataset and availability of metrics to ensure robust quality of generated synthetic datasets. Lastly, we discuss the potential merits, challenges and future direction of synthetic data within the pharmaceutical industry and the considerations for this privacy enhancing technology.

scholarly journals Improving Data Transparency and Accessibility in the Research Community through the Construction of Accurately Simulated Time-to-Event Datasets

2021 ◽  
Author(s):  
Aiden Smith ◽  
Paul Lambert ◽  
Mark Rutherford
Keyword(s):  
Data Privacy ◽  
Synthetic Data ◽  
Original Data ◽  
Survival Times ◽  
Time To Event ◽  
Research Information ◽  
Cancer Dataset ◽  
Individual Level ◽  
Original Dataset ◽  
Real World Datasets

Abstract BackgroundA lack of availability of data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, accurate time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on.MethodsThis paper presents methods to accurately replicate the covariate patterns and survival times found in real-world datasets using simulation techniques, without compromising individual patient identifiability. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to simulate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented.ResultsWe successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research. ConclusionsWe evaluate the effectiveness of the simulation methods for constructing synthetic data, as well as providing evidence that it is almost impossible that a given patient from the original data could be identified from their individual unique date information. Simulated datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research.

scholarly journals Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
David R. McClure ◽  
Jerome P. Reiter
Keyword(s):  
Synthetic Data ◽  
Stochastic Perturbation ◽  
Secondary Data ◽  
Quality Measures ◽  
Individual Level ◽  
The Public ◽  
Level Data ◽  
Synthetic Datasets ◽  
Automated Feedback

When releasing individual-level data to the public, statistical agencies typically alter data values to protect the confidentiality of individuals’ identities and sensitive attributes. When data undergo substantial perturbation, secondary data analysts’ inferences can be distorted in ways that they typically cannot determine from the released data alone. This is problematic, in that analysts have no idea if they should trust the results based on the altered data.To ameliorate this problem, agencies can establish verification servers, which are remote computers that analysts query for measures of the quality of inferences obtained from disclosure-protected data. The reported quality measures reflect the similarity between the analysis done with the altered data and the analysis done with the confidential data. However, quality measures can leak information about the confidential values, so that they too must be subject to disclosure protections. In this article, we discuss several approaches to releasing quality measures for verification servers when the public use data are generated via multiple imputation, also known as synthetic data. The methods can be modified for other stochastic perturbation methods.

scholarly journals A Privacy Preserving Algorithm to Release Sparse High-dimensional Histograms

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Bai Li ◽  
Vishesh Karwa ◽  
Aleksandra Slavković ◽  
Rebecca Carter Steorts
Keyword(s):  
Differential Privacy ◽  
Synthetic Data ◽  
Simulated Data ◽  
Estimation Algorithm ◽  
High Dimensional ◽  
Data Sets ◽  
Original Dataset ◽  
Histogram Estimation ◽  
Synthetic Datasets ◽  
Sparsity Structure

Differential privacy has emerged as a popular model to provably limit privacy risks associated with a given data release. However releasing high dimensional synthetic data under differential privacy remains a challenging problem. In this paper, we study the problem of releasing synthetic data in the form of a high dimensional histogram under the constraint of differential privacy.We develop an $(\epsilon, \delta)$-differentially private categorical data synthesizer called \emph{Stability Based Hashed Gibbs Sampler} (SBHG). SBHG works by combining a stability based sparse histogram estimation algorithm with Gibbs sampling and feature selection to approximate the empirical joint distribution of a discrete dataset. SBHG offers a competitive alternative to state-of-the art synthetic data generators while preserving the sparsity structure of the original dataset, which leads to improved statistical utility as illustrated on simulated data. Finally, to study the utility of the resulting synthetic data sets generated by SBHG, we also perform logistic regression using the synthetic datasets and compare the classification accuracy with those from using the original dataset.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

scholarly journals Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data

2021 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Georg Steinbuss ◽  
Klemens Böhm
Keyword(s):  
Outlier Detection ◽  
Synthetic Data ◽  
Real Data ◽  
Detection Methods ◽  
Quality Of Data ◽  
Benchmark Data ◽  
Core Idea ◽  
Generic Process ◽  
Unsupervised Outlier Detection

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic data. In this work, we propose a generic process for the generation of datasets for such benchmarking. The core idea is to reconstruct regular instances from existing real-world benchmark data while generating outliers so that they exhibit insightful characteristics. We propose and describe a generic process for the benchmarking of unsupervised outlier detection, as sketched so far. We then describe three instantiations of this generic process that generate outliers with specific characteristics, like local outliers. To validate our process, we perform a benchmark with state-of-the-art detection methods and carry out experiments to study the quality of data reconstructed in this way. Next to showcasing the workflow, this confirms the usefulness of our proposed process. In particular, our process yields regular instances close to the ones from real data. Summing up, we propose and validate a new and practical process for the benchmarking of unsupervised outlier detection.

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

Layered Manufacturing: Challenges and Opportunities

2002 ◽  
Vol 758 ◽  
Author(s):  
Khershed P. Cooper
Keyword(s):  
Science Research ◽  
Scientific Activity ◽  
Layered Manufacturing ◽  
Full Potential ◽  
Local Composition ◽  
Photosensitive Polymers ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Future Direction

ABSTRACTLayered Manufacturing (LM) refers to computer-aided manufacturing processes in which parts are made in sequential layers relatively quickly. Parts that are produced by LM can be formed from a wide range of materials such as photosensitive polymers, metals and ceramics in sizes from a centimeter to a few meters with sub-millimeter feature resolutions. LM has found use in diverse areas including biomedical engineering, pharmaceuticals, aerospace, defense, electronics and design engineering. The promise of LM is the capability to make customized complex-shaped functional parts without specialized tooling and without assembly. LM is still a few years away from fully realizing its promise but its potential for manufacturing remains high. A few of the fundamental challenges in materials processing confronting the community are improving the quality of the surface finish, eliminating residual stress, controlling local composition and microstructure, achieving fine feature size and dimensional tolerance and accelerating processing speed. Until these challenges are met, the applicability of LM and its commercialization will be restricted. Sustained scientific activity in LM has advanced over the past decade into many different areas of manufacturing and has enabled exploration of novel processes and development of hybrid processes. The research community of today has the opportunity to shape the future direction of science research to realize the full potential of LM.

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