Algorithms For Data And Computation Privacy

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Algorithms For Data And Computation Privacy

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Author : Alex X. Liu
language : en
Publisher: Springer Nature
Release Date : 2020-11-28

Algorithms For Data And Computation Privacy written by Alex X. Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-28 with Computers categories.

This book introduces the state-of-the-art algorithms for data and computation privacy. It mainly focuses on searchable symmetric encryption algorithms and privacy preserving multi-party computation algorithms. This book also introduces algorithms for breaking privacy, and gives intuition on how to design algorithm to counter privacy attacks. Some well-designed differential privacy algorithms are also included in this book. Driven by lower cost, higher reliability, better performance, and faster deployment, data and computing services are increasingly outsourced to clouds. In this computing paradigm, one often has to store privacy sensitive data at parties, that cannot fully trust and perform privacy sensitive computation with parties that again cannot fully trust. For both scenarios, preserving data privacy and computation privacy is extremely important. After the Facebook–Cambridge Analytical data scandal and the implementation of the General Data Protection Regulation by European Union, users are becoming more privacy aware and more concerned with their privacy in this digital world. This book targets database engineers, cloud computing engineers and researchers working in this field. Advanced-level students studying computer science and electrical engineering will also find this book useful as a reference or secondary text.

The Algorithmic Foundations Of Differential Privacy

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Author : Cynthia Dwork
language : en
Publisher:
Release Date : 2014

The Algorithmic Foundations Of Differential Privacy written by Cynthia Dwork and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Computers categories.

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 Data Mining

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Author : Charu C. Aggarwal
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-06-10

Privacy Preserving Data Mining written by Charu C. Aggarwal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-10 with Computers categories.

Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals, causing concerns that personal data may be used for a variety of intrusive or malicious purposes. Privacy-Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques. 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. Privacy-Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science, and is also suitable for industry practitioners.

Pattern Recognition Algorithms For Data Mining

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Author : Sankar K. Pal
language : en
Publisher: CRC Press
Release Date : 2004-05-27

Pattern Recognition Algorithms For Data Mining written by Sankar K. Pal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-27 with Computers categories.

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Privacy

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Author : Michael Filimowicz
language : en
Publisher: Routledge
Release Date : 2022-02-23

Privacy written by Michael Filimowicz and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-23 with Social Science categories.

Privacy: Algorithms and Society focuses on encryption technologies and privacy debates in journalistic crypto-cultures, countersurveillance technologies, digital advertising, and cellular location data. Important questions are raised such as: How much information will we be allowed to keep private through the use of encryption on our computational devices? What rights do we have to secure and personalized channels of communication, and how should those be balanced by the state’s interests in maintaining order and degrading the capacity of criminals and rival state actors to organize through data channels? What new regimes may be required for states to conduct digital searches, and how does encryption act as countersurveillance? How have key debates relied on racialized social constructions in their discourse? What transformations in journalistic media and practices have occurred with the development of encryption tools? How are the digital footprints of consumers tracked and targeted? Scholars and students from many backgrounds as well as policy makers, journalists, and the general reading public will find a multidisciplinary approach to questions of privacy and encryption encompassing research from Communication, Sociology, Critical Data Studies, and Advertising and Public Relations.

Privacy Preserving Data Mining

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Author : Jaideep Vaidya
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-11-29

Privacy Preserving Data Mining written by Jaideep Vaidya and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-29 with Computers categories.

Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area. Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.

Data Privacy And Security

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Author : David Salomon
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Data Privacy And Security written by David Salomon and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.

Covering classical cryptography, modern cryptography, and steganography, this volume details how data can be kept secure and private. Each topic is presented and explained by describing various methods, techniques, and algorithms. Moreover, there are numerous helpful examples to reinforce the reader's understanding and expertise with these techniques and methodologies. Features & Benefits: * Incorporates both data encryption and data hiding * Supplies a wealth of exercises and solutions to help readers readily understand the material * Presents information in an accessible, nonmathematical style * Concentrates on specific methodologies that readers can choose from and pursue, for their data-security needs and goals * Describes new topics, such as the advanced encryption standard (Rijndael), quantum cryptography, and elliptic-curve cryptography. The book, with its accessible style, is an essential companion for all security practitioners and professionals who need to understand and effectively use both information hiding and encryption to protect digital data and communications. It is also suitable for self-study in the areas of programming, software engineering, and security.

Cloud Computing Data Auditing Algorithm

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Author : Manjur Kolhar
language : en
Publisher: Notion Press
Release Date : 2017-05-09

Cloud Computing Data Auditing Algorithm written by Manjur Kolhar and has been published by Notion Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-09 with Computers categories.

Many Cloud data auditing algorithms have been proposed to maintain the integrity and privacy of data held in the Cloud. In this book, we present a survey of the state of the art and research of Cloud data auditing techniques with a brief introduction of the basic cloud computing concepts, its architecture and security issues. This book presents an overview of the various methods presently used to perform Cloud data auditing, mostly focusing on integrity and privacy.

Algorithms And Data Structures For Massive Datasets

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Author : Dzejla Medjedovic
language : en
Publisher: Simon and Schuster
Release Date : 2022-08-16

Algorithms And Data Structures For Massive Datasets written by Dzejla Medjedovic and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-16 with Computers categories.

Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting

The Top Ten Algorithms In Data Mining

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Author : Xindong Wu
language : en
Publisher: CRC Press
Release Date : 2009-04-09

The Top Ten Algorithms In Data Mining written by Xindong Wu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-09 with Business & Economics categories.

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses. By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.