Linking Sensitive Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Linking Sensitive Data write by Peter Christen. This book was released on 2020. Linking Sensitive Data available in PDF, EPUB and Kindle. This book provides modern technical answers to the legal requirements of pseudonymisation as recommended by privacy legislation. It covers topics such as modern regulatory frameworks for sharing and linking sensitive information, concepts and algorithms for privacy-preserving record linkage and their computational aspects, practical considerations such as dealing with dirty and missing data, as well as privacy, risk, and performance assessment measures. Existing techniques for privacy-preserving record linkage are evaluated empirically and real-world application examples that scale to population sizes are described. The book also includes pointers to freely available software tools, benchmark data sets, and tools to generate synthetic data that can be used to test and evaluate linkage techniques. This book consists of fourteen chapters grouped into four parts, and two appendices. The first part introduces the reader to the topic of linking sensitive data, the second part covers methods and techniques to link such data, the third part discusses aspects of practical importance, and the fourth part provides an outlook of future challenges and open research problems relevant to linking sensitive databases. The appendices provide pointers and describe freely available, open-source software systems that allow the linkage of sensitive data, and provide further details about the evaluations presented. A companion Web site at https://dmm.anu.edu.au/lsdbook2020 provides additional material and Python programs used in the book. This book is mainly written for applied scientists, researchers, and advanced practitioners in governments, industry, and universities who are concerned with developing, implementing, and deploying systems and tools to share sensitive information in administrative, commercial, or medical databases. The Book describes how linkage methods work and how to evaluate their performance. It covers all the major concepts and methods and also discusses practical matters such as computational efficiency, which are critical if the methods are to be used in practice - and it does all this in a highly accessible way! David J. Hand, Imperial College, London.
Data Matching
Data Matching - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Matching write by Peter Christen. This book was released on 2012-07-04. Data Matching available in PDF, EPUB and Kindle. Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.
Understanding and Mitigating Privacy Risks Raised by Record Linkage
Understanding and Mitigating Privacy Risks Raised by Record Linkage - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Understanding and Mitigating Privacy Risks Raised by Record Linkage write by Imrul Chowdhury Anindya. This book was released on 2020. Understanding and Mitigating Privacy Risks Raised by Record Linkage available in PDF, EPUB and Kindle. Record linkage is the process of combining data belonging to the same entity but possibly lacking a common identifier and often dispersed in multiple repositories. Despite having its legitimate usage in data de-duplication and data integration, record linkage poses serious privacy risk as numerous demonstrative attacks show that dedicated adversaries can use this technique to infer sensitive information about their target entities. In this dissertation, we analyze the extent to which record linkage poses privacy risk at the current state of data availability and data-sharing policies and then present some insights on how to mitigate this risk. We start by discussing the “missing value problem” in record linkage. We analyze the impact of this problem on a few widely used “blocking methods” that play an important role in the timely completion of any sizable record linkage task. By experimenting on real-world datasets, we provide guidance on choosing the appropriate blocking methods along with their parameters in the presence of such problem. Next, we discuss a cost-constrained multi-dataset linkage problem in which the adversaries try to link only a subset of the available datasets to limit their purchasing cost while optimizing the expected utility in terms of the quality of the inferred sensitive information. We propose a few metadata-driven heuristics that the adversaries could use to optimally choose the datasets for linkage. By simulating a few realistic scenarios for this multi-dataset linkage task, we analyze the efficacy of the proposed heuristics and the extent to which the adversaries are able to find sensitive information accurately and thereby quantify the privacy risk. Finally, we present how machine learning models could be utilized to predict undocumented personal attributes which if included in the record linkage process may further increase the privacy risks. In particular, we use machine learning to reveal the real-world identities of online entities (e.g., Twitter users) with the help of an auxiliary data source that already contains their identities (e.g., voter registration database). We train the state-of-the-art machine learning models on the unstructured data (e.g., tweets) generated by the online entities to predict their personal attributes (e.g., gender, age, race, political orientation etc.) and combine these predicted attributes with their given attributes (e.g., Twitter name, location etc.) to “re-identify” them to the auxiliary data source. We analyze the severity of the re-identification risk raised by this technique and impart some insights on how to mitigate this risk. We believe our work would provide a deeper understanding of the privacy risk posed by record linkage and guide policymakers in their endeavors for taking preventive measures.
Secure Computation of K-anonymous Distributed Data
Secure Computation of K-anonymous Distributed Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Secure Computation of K-anonymous Distributed Data write by Bradley Malin. This book was released on 2004. Secure Computation of K-anonymous Distributed Data available in PDF, EPUB and Kindle. Abstract: "In a distributed environment, such as the World Wide Web, an individual leaves behind personal data at many different locations. To protect the privacy of an individual's sensitive information, locations make separate releases of identifiable data (e.g. name or social security number), and sensitive data (e.g. visitor's IP address). To the releasing location the data appears unlinkable, however, links can be established when multiple locations' releases are available. This problem, known as trail re-identification, manifests when an individual's location-visit patterns are reconstructed from, and linked between, sensitive and identifiable releases. In this paper, we present a protocol that enables locations to prevent trail re-identification without revealing identified or sensitive data. Instead, locations communicate encrypted versions of their datasets, such that decrypted data is never revealed until completion of the protocol. Via the protocol, every piece of sensitive data, released from any set of locations, is guaranteed to be equally relatable to at least k identities, or is k-anonymous."
Linking Data for Health Services Research
Linking Data for Health Services Research - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Linking Data for Health Services Research write by Agency for and Quality. This book was released on 2014-12-31. Linking Data for Health Services Research available in PDF, EPUB and Kindle. Health registries greatly enhance health services research, especially when linked with other data sources such as administrative claims. Recently, concerns about patient privacy and data security have produced policies such as the Health Insurance Portability and Accountability Act (HIPAA) that reduce the availability of sensitive identifying information. In this context, the development of effective record linkage approaches for varying scenarios of data availability is critical. This report presents a conceptual framework and instructional information that scientifically describe the strengths and limitations of different approaches to record linkage of registries to other data sources. The report defines the requirements for high-quality record linkage of registries to other data sources and describes the strengths and limitations of different approaches. By explaining the spectrum of activities involved, it serves as an instructional guide for researchers designing new CER studies using patient registries linked with other secondary data sources. Through this report, we provide an overview of linkage from registries to administrative claims, including considerations for researchers, data managers, information technology managers, and other stakeholders who are likely to be involved in the process of data linkage. We also apply the data linkage framework to a real-world problem and discuss the results.
