Database Anonymization

If merging sensitive data from several sources, the incoming data may already
have been anonymized at the source (in fact they probably should). Hence, the
ability to link anonymized records from several sources that correspond to the
same ...

Database Anonymization

Author: Josep Domingo-Ferrer

Publisher: Morgan & Claypool Publishers

ISBN: 1627058443

Page: 136

View: 164

The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guarantees they offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer.

Anonymization of Electronic Medical Records to Support Clinical Analysis

Academics and other research scientists will also find the book invaluable.

Anonymization of Electronic Medical Records to Support Clinical Analysis

Author: Aris Gkoulalas-Divanis

Publisher: Springer Science & Business Media

ISBN: 1461456673

Page: 72

View: 859

Anonymization of Electronic Medical Records to Support Clinical Analysis closely examines the privacy threats that may arise from medical data sharing, and surveys the state-of-the-art methods developed to safeguard data against these threats. To motivate the need for computational methods, the book first explores the main challenges facing the privacy-protection of medical data using the existing policies, practices and regulations. Then, it takes an in-depth look at the popular computational privacy-preserving methods that have been developed for demographic, clinical and genomic data sharing, and closely analyzes the privacy principles behind these methods, as well as the optimization and algorithmic strategies that they employ. Finally, through a series of in-depth case studies that highlight data from the US Census as well as the Vanderbilt University Medical Center, the book outlines a new, innovative class of privacy-preserving methods designed to ensure the integrity of transferred medical data for subsequent analysis, such as discovering or validating associations between clinical and genomic information. Anonymization of Electronic Medical Records to Support Clinical Analysis is intended for professionals as a reference guide for safeguarding the privacy and data integrity of sensitive medical records. Academics and other research scientists will also find the book invaluable.

The Complete Book of Data Anonymization

The Complete Book of Data Anonymization: From Planning to Implementation supplies a 360-degree view of data privacy protection using data anonymization.

The Complete Book of Data Anonymization

Author: Balaji Raghunathan

Publisher: CRC Press

ISBN: 1439877319

Page: 267

View: 715

The Complete Book of Data Anonymization: From Planning to Implementation supplies a 360-degree view of data privacy protection using data anonymization. It examines data anonymization from both a practitioner's and a program sponsor's perspective. Discussing analysis, planning, setup, and governance, it illustrates the entire process of adapting an

Anonymizing Health Data

Updated as of August 2014, this practical book will demonstrate proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity.

Anonymizing Health Data

Author: Khaled El Emam

Publisher: "O'Reilly Media, Inc."

ISBN: 1449363032

Page: 228

View: 575

Updated as of August 2014, this practical book will demonstrate proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets. Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors’ experiences with a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others. Understand different methods for working with cross-sectional and longitudinal datasets Assess the risk of adversaries who attempt to re-identify patients in anonymized datasets Reduce the size and complexity of massive datasets without losing key information or jeopardizing privacy Use methods to anonymize unstructured free-form text data Minimize the risks inherent in geospatial data, without omitting critical location-based health information Look at ways to anonymize coding information in health data Learn the challenge of anonymously linking related datasets

Data Privacy

The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc.

Data Privacy

Author: Nataraj Venkataramanan

Publisher: CRC Press

ISBN: 1498721052

Page: 212

View: 990

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.

Data Protection and Data Access

Reports from Ten Countries on Data Protection and Data Access in Social
Research, with an Annotated International ... ABSTRACT : The author describes
an attempt to de - anonymize a statistical database by using another database to
 ...

Data Protection and Data Access

Author: Paul de Guchteneire

Publisher: Royal Netherlands Academy of

ISBN:

Page: 273

View: 898

Paperback. This publication deals with data protection and data access in the social sciences. The first part consists of reports from ten countries, covering country-specific legislation, and discussing problems and solutions concerning data access for research purposes. Subjects considered include practical examples of new methods to give access to machine readable data files, and the implications of privacy legislation and data protection for social science research. The second part consists of an international bibliography on the subject.The reports and bibliography form an update to the subject of data protection and data access for research at a time that overall computerization of personal information has become a reality and many countries have revised their legislation on privacy and data access.

Proceedings of the Twenty third ACM SIGMOD SIGACT SIGART Symposium on Principles of Database Systems

... The technique of k - anonymization has been proposed in the literature as an
alternative way to release public information , while ensuring both data privacy
and data integrity . We prove that two general versions of optimal k -
anonymization ...

Proceedings of the Twenty third ACM SIGMOD SIGACT SIGART Symposium on Principles of Database Systems

Author: Association for Computing Machinery. Special Interest Group on Management of Data

Publisher:

ISBN: 9781581138580

Page: 343

View: 191

Privacy Technologies and Policy

This book constitutes the thoroughly refereed post-conference proceedings of the 5th Annual Privacy Forum, APF 2017, held in Vienna, Austria, in June 2017.

Privacy Technologies and Policy

Author: Erich Schweighofer

Publisher: Springer

ISBN: 3319672800

Page: 231

View: 201

This book constitutes the thoroughly refereed post-conference proceedings of the 5th Annual Privacy Forum, APF 2017, held in Vienna, Austria, in June 2017. The 12 revised full papers were carefully selected from 41 submissions on the basis of significance, novelty, and scientific quality. These selected papers are organized in three different chapters corresponding to the conference sessions. The first chapter, “Data Protection Regulation”, discusses topics concerning big genetic data, a privacy-preserving European identity ecosystem, the right to be forgotten und the re-use of privacy risk analysis. The second chapter, “Neutralisation and Anonymization”, discusses neutralisation of threat actors, privacy by design data exchange between CSIRTs, differential privacy and database anonymization. Finally, the third chapter, “Privacy Policies in Practice”, discusses privacy by design, privacy scores, privacy data management in healthcare and trade-offs between privacy and utility.

Transactions on Large Scale Data and Knowledge Centered Systems XXIV

Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control.

Transactions on Large Scale Data  and Knowledge Centered Systems XXIV

Author: Abdelkader Hameurlain

Publisher: Springer

ISBN: 3662492148

Page: 221

View: 992

This, the 24th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers presented at the 25th International Conference on Database and Expert Systems Applications, DEXA 2014, held in Munich, Germany, in September 2014. Following the conference, and two further rounds of reviewing and selection, six extended papers and one invited keynote paper were chosen for inclusion in this special issue. Topics covered include systems modeling, similarity search, bioinformatics, data pricing, k-nearest neighbor querying, database replication, and data anonymization.

The Journal of Biolaw Business

Whether and how the data should be anonymized will depend on the situation . ...
Computerized database endeavors are not only serving health care and
research but are in some ways driving the computerization of health records .
Adverse ...

The Journal of Biolaw   Business

Author:

Publisher:

ISBN:

Page:

View: 607

Transactions on Large Scale Data and Knowledge Centered Systems XXXVIII

Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control.

Transactions on Large Scale Data  and Knowledge Centered Systems XXXVIII

Author: Abdelkader Hameurlain

Publisher: Springer

ISBN: 3662583844

Page: 173

View: 854

This, the 38th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of six papers selected from the 68 contributions presented at the 27th International Conference on Database and Expert Systems Applications, DEXA 2016, held in Porto, Portugal, in September 2016. Topics covered include query personalization in databases, data anonymization, similarity search, computational methods for entity resolution, array-based computations in big data analysis, and pattern mining.

Cube space Data Mining

In Proceedings of the 22nd International Conference on Data Engineering ( ICDE
' 06 ) , pp . 25 . LeFevre , K . , De Witt , D . , and Ramakrishnan , R . 2006b .
Workload - Aware Anonymization . In Proceedings of the 12th ACM SIGKDD ...

Cube space Data Mining

Author: Bee-Chung Chen

Publisher:

ISBN:

Page: 257

View: 192

International Conference and Workshop on Risk Analysis in Process Safety

Facility or Company Use of Data - Anonymized + Company Data - -- Data Morgo
Data Guideline Protocols Data Quality Control CCPS Central Industry Data
Anonymization -- Devolop Generic Handbook CCPS Anonymized Database ...

International Conference and Workshop on Risk Analysis in Process Safety

Author:

Publisher: Amer Inst of Chemical Engineers

ISBN:

Page: 806

View: 685

Comprises papers from a conference, held in October 1997, and co-sponsored by the US Environmental Protection Agency (USEPA), UK Health and Safety Executive, and European Federation of Chemical Engineering. The text concentrates on the current state of risk assessment as perceived from a broad selection of industry and regulatory viewpoints, and introduces discussion of the recent USEPA Risk Management Program interpretation tools.

Personalising Privacy Contraints in Generalization based Anonymization Models

To regain indivuals'trust, it becomes essential to propose user empowerment solutions, that is to say allowing individuals to control the privacy parameter used to make computations over their microdata.This work proposes a novel concept of ...

Personalising Privacy Contraints in Generalization based Anonymization Models

Author: Axel Michel

Publisher:

ISBN:

Page: 199

View: 692

The benefit of performing Big data computations over individual's microdata is manifold, in the medical, energy or transportation fields to cite only a few, and this interest is growing with the emergence of smart-disclosure initiatives around the world. However, these computations often expose microdata to privacy leakages, explaining the reluctance of individuals to participate in studies despite the privacy guarantees promised by statistical institutes. To regain indivuals'trust, it becomes essential to propose user empowerment solutions, that is to say allowing individuals to control the privacy parameter used to make computations over their microdata.This work proposes a novel concept of personalized anonymisation based on data generalization and user empowerment.Firstly, this manuscript proposes a novel approach to push personalized privacy guarantees in the processing of database queries so that individuals can disclose different amounts of information (i.e. data at different levels of accuracy) depending on their own perception of the risk. Moreover, we propose a decentralized computing infrastructure based on secure hardware enforcing these personalized privacy guarantees all along the query execution process.Secondly, this manuscript studies the personalization of anonymity guarantees when publishing data. We propose the adaptation of existing heuristics and a new approach based on constraint programming. Experiments have been done to show the impact of such personalization on the data quality. Individuals'privacy constraints have been built and realistically using social statistic studies.

IBM Systems Journal

Abstraction - ▻ Query External Database ,. Extraction L_ _ > Deidentification
Figure 1 BIMS data flow known as anonymization) generally involves the removal
of information that can identify the person associated with a medical record.

IBM Systems Journal

Author:

Publisher:

ISBN:

Page:

View: 182