Privacy Preserving Genomic Data Publishing Via Differential Privacy

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Privacy-preserving Genomic Data Publishing Via Differential Privacy

Author : Tanya Khatri
Publisher :
ISBN 13 :
Total Pages : 68 pages
Book Rating : 4.:/5 (123 download)

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Book Synopsis Privacy-preserving Genomic Data Publishing Via Differential Privacy by : Tanya Khatri

Download or read book Privacy-preserving Genomic Data Publishing Via Differential Privacy written by Tanya Khatri and published by . This book was released on 2018 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Privacy-preserving data publishing is a mechanism for sharing data while ensuring the privacy of individuals is preserved in the published data and utility is maintained for data mining and analysis. There is a huge need for sharing genomic data to advance medical and health research. However, since genomic data is highly sensitive and the ultimate identifier, it is a big challenge to publish genomic data while protecting the privacy of individuals in the data. In this thesis, we address the aforementioned challenge by presenting an approach for privacy-preserving genomic data publishing via differentially-private suffix tree. The proposed algorithm uses a top-down approach and utilizes Laplace mechanism to divide the raw genomic data into disjoint partitions, and then normalize the partitioning structure to ensure consistency and maintain utility. The output of our algorithm is a differentially-private suffix tree, a data structure most suitable for efficient search on genomic data. We experiment on real-life genomic data obtained from the Human Genome Privacy Challenge project, and we show that our approach is efficient, scalable, and achieves high utility with respect to genomic sequence matching count queries."--Boise State University ScholarWorks.

The Algorithmic Foundations of Differential Privacy

Author : Cynthia Dwork
Publisher :
ISBN 13 : 9781601988188
Total Pages : 286 pages
Book Rating : 4.9/5 (881 download)

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Book Synopsis The Algorithmic Foundations of Differential Privacy by : Cynthia Dwork

Download or read book The Algorithmic Foundations of Differential Privacy written by Cynthia Dwork and published by . This book was released on 2014 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.

Privacy-preserving Trajectory Data Publishing Via Differential Privacy

Author : Ishita Dwivedi
Publisher :
ISBN 13 :
Total Pages : 72 pages
Book Rating : 4.:/5 (114 download)

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Book Synopsis Privacy-preserving Trajectory Data Publishing Via Differential Privacy by : Ishita Dwivedi

Download or read book Privacy-preserving Trajectory Data Publishing Via Differential Privacy written by Ishita Dwivedi and published by . This book was released on 2017 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Over the past decade, the collection of data by individuals, businesses and government agencies has increased tremendously. Due to the widespread of mobile computing and the advances in location-acquisition techniques, an immense amount of data concerning the mobility of moving objects have been generated. The movement data of an object (e.g. individual) might include specific information about the locations it visited, the time those locations were visited, or both. While it is beneficial to share data for the purpose of mining and analysis, data sharing might risk the privacy of the individuals involved in the data. Privacy-Preserving Data Publishing (PPDP) provides techniques that utilize several privacy models for the purpose of publishing useful information while preserving data privacy. The objective of this thesis is to answer the following question: How can a data owner publish trajectory data while simultaneously safeguarding the privacy of the data and maintaining its usefulness? We propose an algorithm for anonymizing and publishing trajectory data that ensures the output is differentially private while maintaining high utility and scalability. Our solution comprises a twofold approach. First, we generalize trajectories by generalizing and then partitioning the timestamps at each location in a differentially private manner. Next, we add noise to the real count of the generalized trajectories according to the given privacy budget to enforce differential privacy. As a result, our approach achieves an overall epsilon-differential privacy on the output trajectory data. We perform experimental evaluation on real-life data, and demonstrate that our proposed approach can effectively answer count and range queries, as well as mining frequent sequential patterns. We also show that our algorithm is efficient w.r.t. privacy budget and number of partitions, and also scalable with increasing data size."--Boise State University ScholarWorks.

Introduction to Privacy-Preserving Data Publishing

Author : Benjamin C.M. Fung
Publisher : CRC Press
ISBN 13 : 1420091506
Total Pages : 374 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Introduction to Privacy-Preserving Data Publishing by : Benjamin C.M. Fung

Download or read book Introduction to Privacy-Preserving Data Publishing written by Benjamin C.M. Fung and published by CRC Press. This book was released on 2010-08-02 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Int

Privacy-Preserving Data Publishing

Author : Bee-Chung Chen
Publisher : Now Publishers Inc
ISBN 13 : 1601982763
Total Pages : 183 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Privacy-Preserving Data Publishing by : Bee-Chung Chen

Download or read book Privacy-Preserving Data Publishing written by Bee-Chung Chen and published by Now Publishers Inc. This book was released on 2009-10-14 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is dedicated to those who have something to hide. It is a book about "privacy preserving data publishing" -- the art of publishing sensitive personal data, collected from a group of individuals, in a form that does not violate their privacy. This problem has numerous and diverse areas of application, including releasing Census data, search logs, medical records, and interactions on a social network. The purpose of this book is to provide a detailed overview of the current state of the art as well as open challenges, focusing particular attention on four key themes: RIGOROUS PRIVACY POLICIES Repeated and highly-publicized attacks on published data have demonstrated that simplistic approaches to data publishing do not work. Significant recent advances have exposed the shortcomings of naive (and not-so-naive) techniques. They have also led to the development of mathematically rigorous definitions of privacy that publishing techniques must satisfy; METRICS FOR DATA UTILITY While it is necessary to enforce stringent privacy policies, it is equally important to ensure that the published version of the data is useful for its intended purpose. The authors provide an overview of diverse approaches to measuring data utility; ENFORCEMENT MECHANISMS This book describes in detail various key data publishing mechanisms that guarantee privacy and utility; EMERGING APPLICATIONS The problem of privacy-preserving data publishing arises in diverse application domains with unique privacy and utility requirements. The authors elaborate on the merits and limitations of existing solutions, based on which we expect to see many advances in years to come.

Differential Privacy and Applications

Author : Tianqing Zhu
Publisher : Springer
ISBN 13 : 3319620045
Total Pages : 243 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Differential Privacy and Applications by : Tianqing Zhu

Download or read book Differential Privacy and Applications written by Tianqing Zhu and published by Springer. This book was released on 2017-08-22 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.

Privacy-Preserving Data Mining

Author : Charu C. Aggarwal
Publisher : Springer Science & Business Media
ISBN 13 : 0387709924
Total Pages : 524 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Privacy-Preserving Data Mining by : Charu C. Aggarwal

Download or read book Privacy-Preserving Data Mining written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2008-06-10 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.

Differential Privacy

Author : Ninghui Li
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627052976
Total Pages : 140 pages
Book Rating : 4.6/5 (27 download)

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Book Synopsis Differential Privacy by : Ninghui Li

Download or read book Differential Privacy written by Ninghui Li and published by Morgan & Claypool Publishers. This book was released on 2016-10-26 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks. This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations. We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it. The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.

Privacy-preserving Techniques on Genomic Data

Author : Md Momin Al Aziz
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)

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Book Synopsis Privacy-preserving Techniques on Genomic Data by : Md Momin Al Aziz

Download or read book Privacy-preserving Techniques on Genomic Data written by Md Momin Al Aziz and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic data hold salient information about the characteristics of a living organism. Throughout the last decade, pinnacle developments have given us more accurate and inexpensive methods to retrieve our genome sequences. However, with the advancement of genomic research, there are growing security and privacy concerns regarding collecting, storing, and analyzing such sensitive data. Recent results show that given some background information, it is possible for an adversary to re-identify an individual from a specific genomic dataset. This can reveal the current association or future susceptibility of some diseases for that individual (and sometimes the kinship between individuals), resulting in a privacy violation. This thesis has two parts and proposes several techniques to mitigate the privacy issues relating to genomic data. In our first part, we target the data privacy issues while using any external computational environment. We propose privacy-preserving frameworks to store genomic data in an untrusted computational environment (\textit{i.e.}, cloud). In particular, we employ prefix and suffix tree structures to represent genomic data while keeping them under encryption throughout its computational life-cycle. Therefore, the underlying methods perform different string search queries and arbitrary computations under encryption without requiring access to the raw sensitive data. We also propose a GPU-parallel Fully Homomorphic Encryption framework that optimizes existing algorithms and can perform string distance metrics such as Hamming, Edit distance and Set Maximal Matching. The GPU-parallel framework is 14.4 and 46.81 times faster for standard and matrix multiplications, respectively compared to the existing techniques. The second part of the thesis targets another privacy setting where the outputs from different genomic data analyses are deemed sensitive. Here, we propose several differentially private mechanisms to share partial genome datasets and intermediate statistics providing a strict privacy guarantee. Experimental results demonstrate that the proposed methods are effective for protecting data privacy while computing and analysis of genomic data. Overall, the proposed techniques in this thesis are not specialized for genomic data but can be generalized to protect other types of sensitive data.

Medical Data Privacy Handbook

Author : Aris Gkoulalas-Divanis
Publisher : Springer
ISBN 13 : 3319236334
Total Pages : 854 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Medical Data Privacy Handbook by : Aris Gkoulalas-Divanis

Download or read book Medical Data Privacy Handbook written by Aris Gkoulalas-Divanis and published by Springer. This book was released on 2015-11-26 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook covers Electronic Medical Record (EMR) systems, which enable the storage, management, and sharing of massive amounts of demographic, diagnosis, medication, and genomic information. It presents privacy-preserving methods for medical data, ranging from laboratory test results to doctors’ comments. The reuse of EMR data can greatly benefit medical science and practice, but must be performed in a privacy-preserving way according to data sharing policies and regulations. Written by world-renowned leaders in this field, each chapter offers a survey of a research direction or a solution to problems in established and emerging research areas. The authors explore scenarios and techniques for facilitating the anonymization of different types of medical data, as well as various data mining tasks. Other chapters present methods for emerging data privacy applications and medical text de-identification, including detailed surveys of deployed systems. A part of the book is devoted to legislative and policy issues, reporting on the US and EU privacy legislation and the cost of privacy breaches in the healthcare domain. This reference is intended for professionals, researchers and advanced-level students interested in safeguarding medical data.

Combinatorial Optimization and Applications

Author : Yingshu Li
Publisher : Springer Nature
ISBN 13 : 3030364127
Total Pages : 625 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Combinatorial Optimization and Applications by : Yingshu Li

Download or read book Combinatorial Optimization and Applications written by Yingshu Li and published by Springer Nature. This book was released on 2019-12-06 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the proceedings of the 13th International Conference on Combinatorial Optimization and Applications, COCOA 2019, held in Xiamen, China, in December 2019. The 49 full papers presented in this volume were carefully reviewed and selected from 108 submissions. The papers cover the various topics, including cognitive radio networks, wireless sensor networks, cyber-physical systems, distributed and localized algorithm design and analysis, information and coding theory for wireless networks, localization, mobile cloud computing, topology control and coverage, security and privacy, underwater and underground networks, vehicular networks, information processing and data management, programmable service interfaces, energy-efficient algorithms, system and protocol design, operating system and middleware support, and experimental test-beds, models and case studies.

Protection of Human Genetic Information

Author : Australia. Law Reform Commission
Publisher : Sydney : Australian Law Reform Commission
ISBN 13 : 9780642732118
Total Pages : 441 pages
Book Rating : 4.7/5 (321 download)

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Book Synopsis Protection of Human Genetic Information by : Australia. Law Reform Commission

Download or read book Protection of Human Genetic Information written by Australia. Law Reform Commission and published by Sydney : Australian Law Reform Commission. This book was released on 2001 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: 13. Law enforcement issues

Privacy-preserving Data Publishing Using Deep Learning Techniques

Author : Tanbir Ahmed
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)

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Book Synopsis Privacy-preserving Data Publishing Using Deep Learning Techniques by : Tanbir Ahmed

Download or read book Privacy-preserving Data Publishing Using Deep Learning Techniques written by Tanbir Ahmed and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: According to a recent study, around 99 percent of hospitals across the United States now use electronic health record systems. One of the most common types of EHR data is unstructured textual data and unlocking hidden details from this data is critical for improving current medical practices and research endeavors. However, these textual data contain sensitive information, which could compromise our privacy. Therefore, medical textual data cannot be released publicly without any privacy protection. De-identification is a process of detecting and removing all sensitive information present in EHRs, and it is a necessary step towards privacy-preserving EHR data sharing. Since 2016, we have seen several deep learning-based approaches for de-identification, which achieved over 98% accuracy. However, these models are trained with sensitive information and can unwittingly memorize some of their training data, and a careful analysis of these models can reveal patients' data. This thesis presents two contributions. First, We introduce new methods to de-identify textual based on self-attention mechanism and stacked Recurrent Neural Network. Experimental results on three different datasets show that our model performs better than all state-of-the-art mechanisms irrespective of the dataset. Additionally, our proposed method is significantly faster than existing techniques. We also introduced three utility metrics to judge the quality of the de-identified data. Second, we propose a differentially private ensemble framework for de-identification, allowing medical researchers to collaborate through publicly publishing the de-identification models. Experiments in three different datasets showed competitive results compared to the state-of-the-art methods with guaranteed differential privacy.

Privacy-Preserving Data Publishing

Author : Raymond Chi-Wing Wong
Publisher : Springer Nature
ISBN 13 : 3031018346
Total Pages : 128 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Privacy-Preserving Data Publishing by : Raymond Chi-Wing Wong

Download or read book Privacy-Preserving Data Publishing written by Raymond Chi-Wing Wong and published by Springer Nature. This book was released on 2022-05-31 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions

Privacy Preservation of Genomic and Medical Data

Author : Amit Kumar Tyagi
Publisher : John Wiley & Sons
ISBN 13 : 1394213700
Total Pages : 432 pages
Book Rating : 4.3/5 (942 download)

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Book Synopsis Privacy Preservation of Genomic and Medical Data by : Amit Kumar Tyagi

Download or read book Privacy Preservation of Genomic and Medical Data written by Amit Kumar Tyagi and published by John Wiley & Sons. This book was released on 2023-11-16 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: PRIVACY PRESERVATION of GENOMIC and MEDICAL DATA Discusses topics concerning the privacy preservation of genomic data in the digital era, including data security, data standards, and privacy laws so that researchers in biomedical informatics, computer privacy and ELSI can assess the latest advances in privacy-preserving techniques for the protection of human genomic data. Privacy Preservation of Genomic and Medical Data focuses on genomic data sources, analytical tools, and the importance of privacy preservation. Topics discussed include tensor flow and Bio-Weka, privacy laws, HIPAA, and other emerging technologies like Internet of Things, IoT-based cloud environments, cloud computing, edge computing, and blockchain technology for smart applications. The book starts with an introduction to genomes, genomics, genetics, transcriptomes, proteomes, and other basic concepts of modern molecular biology. DNA sequencing methodology, DNA-binding proteins, and other related terms concerning genomes and genetics, and the privacy issues are discussed in detail. The book also focuses on genomic data sources, analyzing tools, and the importance of privacy preservation. It concludes with future predictions for genomic and genomic privacy, emerging technologies, and applications. Audience Researchers in information technology, data mining, health informatics and health technologies, clinical informatics, bioinformatics, security and privacy in healthcare, as well as health policy developers in public and private health departments and public health.

Tutorials on the Foundations of Cryptography

Author : Yehuda Lindell
Publisher : Springer
ISBN 13 : 331957048X
Total Pages : 461 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Tutorials on the Foundations of Cryptography by : Yehuda Lindell

Download or read book Tutorials on the Foundations of Cryptography written by Yehuda Lindell and published by Springer. This book was released on 2017-04-05 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a graduate textbook of advanced tutorials on the theory of cryptography and computational complexity. In particular, the chapters explain aspects of garbled circuits, public-key cryptography, pseudorandom functions, one-way functions, homomorphic encryption, the simulation proof technique, and the complexity of differential privacy. Most chapters progress methodically through motivations, foundations, definitions, major results, issues surrounding feasibility, surveys of recent developments, and suggestions for further study. This book honors Professor Oded Goldreich, a pioneering scientist, educator, and mentor. Oded was instrumental in laying down the foundations of cryptography, and he inspired the contributing authors, Benny Applebaum, Boaz Barak, Andrej Bogdanov, Iftach Haitner, Shai Halevi, Yehuda Lindell, Alon Rosen, and Salil Vadhan, themselves leading researchers on the theory of cryptography and computational complexity. The book is appropriate for graduate tutorials and seminars, and for self-study by experienced researchers, assuming prior knowledge of the theory of cryptography.

Privacy-Preserving Data Publishing

Author : Raymond Chi-Wing Wong
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608452174
Total Pages : 138 pages
Book Rating : 4.6/5 (84 download)

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Book Synopsis Privacy-Preserving Data Publishing by : Raymond Chi-Wing Wong

Download or read book Privacy-Preserving Data Publishing written by Raymond Chi-Wing Wong and published by Morgan & Claypool Publishers. This book was released on 2010-01-01 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions