Privacy Preserving Genomic Data Publishing Via Differential Privacy PDF Download
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| Author | : Tanya Khatri |
| Publisher | : |
| Total Pages | : 68 |
| Release | : 2018 |
| Genre | : Data encryption (Computer science) |
| ISBN | : |
Download Privacy-preserving Genomic Data Publishing Via Differential Privacy Book in PDF, ePub and Kindle
"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.
| Author | : Cynthia Dwork |
| Publisher | : |
| Total Pages | : 286 |
| Release | : 2014 |
| Genre | : Computers |
| ISBN | : 9781601988188 |
Download The Algorithmic Foundations of Differential Privacy Book in PDF, ePub and Kindle
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.
| Author | : Ishita Dwivedi |
| Publisher | : |
| Total Pages | : 72 |
| Release | : 2017 |
| Genre | : Data mining |
| ISBN | : |
Download Privacy-preserving Trajectory Data Publishing Via Differential Privacy Book in PDF, ePub and Kindle
"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.
| Author | : Benjamin C.M. Fung |
| Publisher | : CRC Press |
| Total Pages | : 374 |
| Release | : 2010-08-02 |
| Genre | : Computers |
| ISBN | : 1420091506 |
Download Introduction to Privacy-Preserving Data Publishing Book in PDF, ePub and Kindle
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
| Author | : Tianqing Zhu |
| Publisher | : Springer |
| Total Pages | : 243 |
| Release | : 2017-08-22 |
| Genre | : Computers |
| ISBN | : 3319620045 |
Download Differential Privacy and Applications Book in PDF, ePub and Kindle
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.
| Author | : Bee-Chung Chen |
| Publisher | : Now Publishers Inc |
| Total Pages | : 183 |
| Release | : 2009-10-14 |
| Genre | : Data mining |
| ISBN | : 1601982763 |
Download Privacy-Preserving Data Publishing Book in PDF, ePub and Kindle
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.
| Author | : Ninghui Li |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 140 |
| Release | : 2016-10-26 |
| Genre | : Computers |
| ISBN | : 1627052976 |
Download Differential Privacy Book in PDF, ePub and Kindle
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.
| Author | : Md Momin Al Aziz |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
Download Privacy-preserving Techniques on Genomic Data Book in PDF, ePub and Kindle
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.
| Author | : Charu C. Aggarwal |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 524 |
| Release | : 2008-06-10 |
| Genre | : Computers |
| ISBN | : 0387709924 |
Download Privacy-Preserving Data Mining Book in PDF, ePub and Kindle
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.
| Author | : Aris Gkoulalas-Divanis |
| Publisher | : Springer |
| Total Pages | : 854 |
| Release | : 2015-11-26 |
| Genre | : Computers |
| ISBN | : 3319236334 |
Download Medical Data Privacy Handbook Book in PDF, ePub and Kindle
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.
