Download or read book Synthetic Datasets for Statistical Disclosure Control written by Jörg Drechsler and published by Springer Science & Business Media. This book was released on 2011-06-24 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to give the reader a detailed introduction to the different approaches to generating multiply imputed synthetic datasets. It describes all approaches that have been developed so far, provides a brief history of synthetic datasets, and gives useful hints on how to deal with real data problems like nonresponse, skip patterns, or logical constraints. Each chapter is dedicated to one approach, first describing the general concept followed by a detailed application to a real dataset providing useful guidelines on how to implement the theory in practice. The discussed multiple imputation approaches include imputation for nonresponse, generating fully synthetic datasets, generating partially synthetic datasets, generating synthetic datasets when the original data is subject to nonresponse, and a two-stage imputation approach that helps to better address the omnipresent trade-off between analytical validity and the risk of disclosure. The book concludes with a glimpse into the future of synthetic datasets, discussing the potential benefits and possible obstacles of the approach and ways to address the concerns of data users and their understandable discomfort with using data that doesn’t consist only of the originally collected values. The book is intended for researchers and practitioners alike. It helps the researcher to find the state of the art in synthetic data summarized in one book with full reference to all relevant papers on the topic. But it is also useful for the practitioner at the statistical agency who is considering the synthetic data approach for data dissemination in the future and wants to get familiar with the topic.

Download or read book Synthetic Data for Confidentiality written by Harold Mantel and published by . This book was released on 2009 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper reviews methodology for creating and analyzing synthetic data files, as implemented for various US Census Bureau survey programs -- particularly a SIPP/SSA/IRS linked file and group quarters data from the American Community Survey. The motivation for synthetic data is to be able to release a public use microdata file suitable for analysis that is not confidential. Methods for data synthesis and proper analysis of synthesized data are reviewed, and issues of confidentiality and analytical validity are discussed. The paper concludes with a literature review.

Download or read book Handbook of Sharing Confidential Data written by Jörg Drechsler and published by CRC Press. This book was released on 2024-10-09 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature—specifically, synthetic data, formal privacy, and secure computation—can be used to manage trade-offs in disclosure risk and data usefulness. Key features: • Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation • Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives • Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy • Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approaches The handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.

Download or read book Privacy in Statistical Databases written by Josep Domingo-Ferrer and published by Springer. This book was released on 2004-06-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Privacy in statistical databases is about ?nding tradeo?s to the tension between the increasing societal and economical demand for accurate information and the legal and ethical obligation to protect the privacy of individuals and enterprises, which are the source of the statistical data. Statistical agencies cannot expect to collect accurate information from individual or corporate respondents unless these feel the privacy of their responses is guaranteed; also, recent surveys of Web users show that a majority of these are unwilling to provide data to a Web site unless they know that privacy protection measures are in place. “Privacy in Statistical Databases2004” (PSD2004) was the ?nal conference of the CASC project (“Computational Aspects of Statistical Con?dentiality”, IST-2000-25069). PSD2004 is in the style of the following conferences: “Stat- tical Data Protection”, held in Lisbon in 1998 and with proceedings published by the O?ce of O?cial Publications of the EC, and also the AMRADS project SDC Workshop, held in Luxemburg in 2001 and with proceedings published by Springer-Verlag, as LNCS Vol. 2316. The Program Committee accepted 29 papers out of 44 submissions from 15 di?erentcountriesonfourcontinents.Eachsubmittedpaperreceivedatleasttwo reviews. These proceedings contain the revised versions of the accepted papers. These papers cover the foundations and methods of tabular data protection, masking methods for the protection of individual data (microdata), synthetic data generation, disclosure risk analysis, and software/case studies.

Download or read book Handbook of Sharing Confidential Data written by Jörg Drechsler and published by CRC Press. This book was released on 2024-10-09 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature—specifically, synthetic data, formal privacy, and secure computation—can be used to manage trade-offs in disclosure risk and data usefulness. Key features: • Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation • Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives • Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy • Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approaches The handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.

Download or read book Innovations in Federal Statistics written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2017-04-21 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm.

Download or read book Practical Synthetic Data Generation written by Khaled El Emam and published by "O'Reilly Media, Inc.". This book was released on 2020-05-19 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions Methods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure

Download or read book Privacy and Synthetic Datasets written by Steven M. Bellovin and published by . This book was released on 2018 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sharing is a virtue, instilled in us from childhood. Unfortunately, when it comes to big data -- i.e., databases possessing the potential to usher in a whole new world of scientific progress -- the legal landscape prefers a hoggish motif. The historic approach to the resulting database-privacy problem has been anonymization, a subtractive technique incurring not only poor privacy results, but also lackluster utility. In anonymization's stead, differential privacy arose; it provides better, near-perfect privacy, but is nonetheless subtractive in terms of utility. Today, another solution is leaning into the fore, synthetic data. Using the magic of machine learning, synthetic data offers a generative, additive approach -- the creation of almost-but-not-quite replica data. In fact, as we recommend, synthetic data may be combined with differential privacy to achieve a best-of-both-worlds scenario. After unpacking the technical nuances of synthetic data, we analyze its legal implications, finding both over and under inclusive applications. Privacy statutes either overweigh or downplay the potential for synthetic data to leak secrets, inviting ambiguity. We conclude by finding that synthetic data is a valid, privacy-conscious alternative to raw data, but is not a cure-all for every situation. In the end, computer science progress must be met with proper policy in order to move the area of useful data dissemination forward.

Download or read book Synthetic Data for Deep Learning written by Sergey I. Nikolenko and published by Springer Nature. This book was released on 2021-06-26 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.

Download or read book Putting People on the Map written by National Research Council and published by National Academies Press. This book was released on 2007-03-22 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precise, accurate spatial information linked to social and behavioral data is revolutionizing social science by opening new questions for investigation and improving understanding of human behavior in its environmental context. At the same time, precise spatial data make it more likely that individuals can be identified, breaching the promise of confidentiality made when the data were collected. Because norms of science and government agencies favor open access to all scientific data, the tension between the benefits of open access and the risks associated with potential breach of confidentiality pose significant challenges to researchers, research sponsors, scientific institutions, and data archivists. Putting People on the Map finds that several technical approaches for making data available while limiting risk have potential, but none is adequate on its own or in combination. This book offers recommendations for education, training, research, and practice to researchers, professional societies, federal agencies, institutional review boards, and data stewards.

Download or read book Confidentiality Disclosure and Data Access written by Pat Doyle and published by Elsevier Science & Technology. This book was released on 2001 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a fundamental tension at the heart of every statistical agency mission. Each is charged with collecting high quality data to inform the national policy and enable statistical research. This necessitates dissemination of both summary and micro data. Each is also charged with protecting the confidentiality of survey respondents. This often necessitates the blurring of the data to reduce the probability of the re-identification of individuals. The tradeoff dilemma, which could well be stated as protecting confidentiality (avoiding disclosure) but optimizing access, has become more complex as both technological advances and public perceptions have altered in an information age. Fortunately, statistical disclosure techniques have kept pace with these changes. This volume is intended to provide a review of new state of the art techniques that directly address these issues from both a theoretical and practical perspective. It provides a review of new research in the area of confidentiality and statistical disclosure techniques. A major section of the book provides an overview of new advances in the field of both economic and demographic data in measuring disclosure risk and information loss. It also presents new information on the different approaches taken by statistical agencies in disseminating data - ranging from licensing agreements , to secure access and provides a new survey of what statistical disclosure techniques are used by statistical agencies around the world. This is complimented by a series of chapters on public perceptions of statistical agency actions, including the results of a new survey on business perceptions. The book concludes with a chapter on the challenges of technology to data protection. National Statistical Agencies, statistical practitioners, thinktanks, research organisations and universities will find this a useful tool.

Download or read book Data Privacy written by Nataraj Venkataramanan and published by CRC Press. This book was released on 2016-10-03 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.

Download or read book Federal Statistics Multiple Data Sources and Privacy Protection written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-01-27 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.

Download or read book Toward a Universal Privacy and Information preserving Framework for Individual Data Exchange written by Nicolas Ruiz and published by . This book was released on 2019 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data on individual subjects, which are increasingly gathered and exchanged, provide a rich amount of information that can inform statistical and policy analysis in a meaningful way. However, due to the legal obligations surrounding such data, this wealth of information is often not fully exploited in order to protect the confidentiality of respondents. The issue is thus the following: how to ensure a sufficient level of data protection to meet releasers' concerns in terms of legal and ethical requirements, while still offering users a reasonable level of information. This question has raised a range concerns about the privacy/information trade-off and has driven a quest for best practices that can be both useful to users but also respectful of individuals' privacy. Statistical disclosure control research has historically provided the analytical apparatus through which the privacy/information trade-off can be assessed and implemented. In recent years, the literature has burgeoned in many directions. In particular, techniques applicable to micro data offer a wide variety of tools to protect the confidentiality of respondents while maximizing the information content of the data released, for the benefit of society at large. Such diversity is undoubtedly useful but has several major drawbacks. In fact, there is currently a clear lack of agreement and clarity as to the appropriate choice of tools in a given context, and as a consequence, there is no comprehensive view (or at best an incomplete one) of the relative performances of the techniques available. The practical scope of current micro data protection methods is not fully exploited precisely because there is no overarching framework: all methods generally carry their own analytical environment, underlying approaches and definitions of privacy and information. Moreover, the evaluation of utility and privacy for each method is metric and data-dependent, meaning that comparisons across different methods and datasets is a daunting task. Against this backdrop, this thesis focuses on establishing some common grounds for individual data anonymization by developing a new, universal approach. Recent contributions to the literature point to the fact that permutations happen to be the essential principle upon which individual data anonymization can be based. In this thesis, we demonstrate that this principle allows for the proposal of a universal analytical environment for data anonymization. The first contribution of this thesis takes an ex-post approach by proposing some universal measures of disclosure risk and information loss that can be computed in a simple fashion and used for the evaluation of any anonymization method, independently of the context under which they operate. In particular, they exhibit distributional independence. These measures establish a common language for comparing different mechanisms, all with potentially varying parametrizations applied to the same data set or to different data sets. The second contribution of this thesis takes an ex-ante approach by developing a new approach to data anonymization. Bringing data anonymization closer to cryptography, it formulates a general cipher based on permutation keys which appears to be equivalent to a general form of rank swapping. Beyond all the existing methods that this cipher can universally reproduce, it also offers a new way to practice data anonymization based on the ex-ante exploration of different permutation structures. The subsequent study of the cipher's properties additionally reveals new insights as to the nature of the task of anonymization taken at a general level of functioning. The final two contributions of this thesis aim at exploring two specific areas using the above results. The first area is longitudinal data anonymization. Despite the fact that the SDC literature offers a wide variety of tools suited to different contexts and data types, there have been very few attempts to deal with the challenges posed by longitudinal data. This thesis thus develops a general framework and some associated metrics of disclosure risk and information loss, tailored to the specific challenges posed by longitudinal data anonymization. Notably, it builds on a permutation approach where the effect of time on time-variant attributes can be seen as an anonymization method that can be captured by temporal permutations. The second area considered is synthetic data. By challenging the information and privacy guarantees of synthetic data, it is shown that any synthetic data set can always be expressed as a permutation of the original data, in a way similar to non-synthetic SDC techniques. In fact, releasing synthetic data sets with the same privacy properties but with an improved level of information appears to be invariably possible as the marginal distributions can always be preserved without increasing risk. On the privacy front, this leads to the consequence that the distinction drawn in the literature between non-synthetic and synthetic data is not so clear-cut. Indeed, it is shown that the practice of releasing several synthetic data sets for a single original data set entails privacy issues that do not arise in non-synthetic anonymization.

Download or read book Finite Mixture Models written by Geoffrey McLachlan and published by John Wiley & Sons. This book was released on 2004-03-22 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.

Download or read book Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series written by John M. Abowd and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Census Bureau's Quarterly Workforce Indicators (QWI) provide detailed quarterly statistics on employment measures such as worker and job flows, tabulated by worker characteristics in various combinations. The data are released for several levels of NAICS industries and geography, the lowest aggregation of the latter being counties. Disclosure avoidance methods are required to protect the information about individuals and businesses that contribute to the underlying data. The QWI disclosure avoidance mechanism we describe here relies heavily on the use of noise infusion through a permanent multiplicative noise distortion factor, used for magnitudes, counts, differences and ratios. There is minimal suppression and no complementary suppressions. To our knowledge, the release in 2003 of the QWI was the first large-scale use of noise infusion in any official statistical product. We show that the released statistics are analytically valid along several critical dimensions measures are unbiased and time series properties are preserved. We provide an analysis of the degree to which confidentiality is protected. Furthermore, we show how the judicious use of synthetic data, injected into the tabulation process, can completely eliminate suppressions, maintain analytical validity, and increase the protection of the underlying confidential data.

Download or read book Improving Access to and Confidentiality of Research Data written by National Research Council and published by National Academies Press. This book was released on 2000-09-11 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improving Access to and Confidentiality of Research Data summarizes a workshop convened by the Committee on National Statistics (CNSTAT) to promote discussion about methods for advancing the often conflicting goals of exploiting the research potential of microdata and maintaining acceptable levels of confidentiality. This report outlines essential themes of the access versus confidentiality debate that emerged during the workshop. Among these themes are the tradeoffs and tensions between the needs of researchers and other data users on the one hand and confidentiality requirements on the other; the relative advantages and costs of data perturbation techniques (applied to facilitate public release) versus restricted access as tools for improving security; and the need to quantify disclosure risksâ€"both absolute and relativeâ€"created by researchers and research data, as well as by other data users and other types of data.