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Author: Kristin Lauter Publisher: Springer Nature ISBN: 303077287X Category : Mathematics Languages : en Pages : 184
Book Description
This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on Homomorphic Encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research. The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear.
Author: Kristin Lauter Publisher: Springer Nature ISBN: 303077287X Category : Mathematics Languages : en Pages : 184
Book Description
This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on Homomorphic Encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research. The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear.
Author: Xun Yi Publisher: Springer ISBN: 3319122290 Category : Computers Languages : en Pages : 136
Book Description
This book introduces the fundamental concepts of homomorphic encryption. From these foundations, applications are developed in the fields of private information retrieval, private searching on streaming data, privacy-preserving data mining, electronic voting and cloud computing. The content is presented in an instructional and practical style, with concrete examples to enhance the reader's understanding. This volume achieves a balance between the theoretical and the practical components of modern information security. Readers will learn key principles of homomorphic encryption as well as their application in solving real world problems.
Author: Sonam Mittal Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
"An Enhanced Homomorphic Encryption Model for Preserving Privacy in Clouds" is a comprehensive and innovative book that explores the application of enhanced homomorphic encryption techniques to safeguard privacy in cloud computing environments. Authored by experts in the field, this book serves as a valuable resource for researchers, professionals, and practitioners interested in leveraging advanced encryption methods to protect sensitive data while harnessing the benefits of cloud computing. In this book, the authors delve into the critical need for privacy preservation in cloud computing, where data is outsourced to remote servers. They introduce an enhanced homomorphic encryption model that enables computations on encrypted data, allowing secure and privacy-preserving data processing in cloud environments. The book covers various aspects of the enhanced homomorphic encryption model, including its theoretical foundations, implementation considerations, and practical applications. Key topics covered in this book include: Privacy challenges in cloud computing: The authors provide a comprehensive overview of the privacy concerns associated with cloud computing, including data leakage, unauthorized access, and privacy breaches. They highlight the need for encryption techniques that allow data to remain confidential even when processed in the cloud. Homomorphic encryption fundamentals: The book offers an in-depth exploration of homomorphic encryption techniques and their applications in cloud computing. Readers gain a solid understanding of fully homomorphic encryption (FHE) and its variations, including partially homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). Enhanced homomorphic encryption model: The authors present their enhanced homomorphic encryption model that incorporates innovative approaches to improve the efficiency, scalability, and security of homomorphic encryption. They discuss techniques such as ciphertext compression, parallelization, and optimization algorithms, ensuring the practicality of the encryption model for real-world cloud computing scenarios. Secure data processing in the cloud: The book explores how the enhanced homomorphic encryption model enables secure and privacy-preserving data processing in cloud environments. It covers various applications, including secure search, data mining, machine learning, and data analytics, demonstrating how encrypted data can be utilized without compromising privacy. Performance considerations and trade-offs: The authors address the performance challenges and trade-offs associated with homomorphic encryption. They discuss factors such as computation complexity, encryption overhead, and key management, providing insights into optimizing the performance of the enhanced homomorphic encryption model. Practical implementation and case studies: The book includes practical implementation considerations and case studies that showcase the deployment and effectiveness of the enhanced homomorphic encryption model in real-world cloud computing scenarios. The case studies cover domains such as healthcare, finance, and sensitive data sharing, illustrating the practicality and benefits of the proposed model. Throughout the book, the authors provide insights, practical examples, and algorithmic explanations to facilitate a deep understanding of the enhanced homomorphic encryption model. By leveraging the power of enhanced homomorphic encryption, "An Enhanced Homomorphic Encryption Model for Preserving Privacy in Clouds" equips its readers with the knowledge and tools necessary to protect sensitive data, preserve privacy, and enable secure cloud-based computations.
Author: Yehuda Lindell Publisher: Springer ISBN: 331957048X Category : Computers Languages : en Pages : 461
Book Description
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.
Author: Ayantika Chatterjee Publisher: Springer ISBN: 9811363935 Category : Technology & Engineering Languages : en Pages : 141
Book Description
This book explores the latest developments in fully homomorphic encryption (FHE), an effective means of performing arbitrary operations on encrypted data before storing it in the ‘cloud’. The book begins by addressing perennial problems like sorting and searching through FHE data, followed by a detailed discussion of the basic components of any algorithm and adapting them to handle FHE data. In turn, the book focuses on algorithms in both non-recursive and recursive versions and discusses their realizations and challenges while operating in the FHE domain on existing unencrypted processors. It highlights potential complications and proposes solutions for encrypted database design with complex queries, including the basic design details of an encrypted processor architecture to support FHE operations in real-world applications.
Author: Suxin Guo Publisher: ISBN: Category : Languages : en Pages : 117
Book Description
Today, the growing concern of privacy issues poses a challenge to the study of sensitive data. In this thesis, we address the learning of private data in two practical scenarios. 1) It is very commonly seen that the same type of data are distributed among multiple parties, and each party has a local portion of the data. For these parties, the learning based only on their own portions of data may lead to small sample problem and generate unsatisfying results. On the other hand, privacy concerns prevent them from exchanging their data and subsequently learning global results from the union of data. In this scenario, we solve the problem with the homomorphic encryption model. Homomorphic encryption enables calculations in the cipher space, which means that some particular operations of data can be conducted even when the data are encrypted. With this technique, we design the privacy preserving solutions for four popular data analysis methods on distributed data, including the Marginal Fisher Analysis (MFA) for dimensionality reduction and classification, the Kruskal-Wallis (KW) statistical test for comparing the distributions of samples, the Markov model for sequence classification and the calculation of Fisher criterion score for informative gene selection. Our solutions allow different parties to perform the algorithms on the union of their data without revealing each party's private information. 2) The other scenario is that, the data holder wants to release some knowledge learned from the sensitive dataset without violating the privacy of individuals participated in the dataset. Although there is no need of direct data exchange in this scenario, publishing the knowledge learned from the data still exposes the participants' private information. Here we adopt the rigorous differential privacy model to protect the individuals' privacy. Specifically, if an algorithm is differentially private, the presence or absence of a data instance in the training dataset would not make much change to the output of the algorithm. In this way, from the released output of the algorithm people cannot gain much information about the individuals participated in the training dataset, and thus the individual privacy is protected. In this scenario, we develop differentially private One Class SVM (1-SVM) models for anomaly detection with theoretical proofs of the privacy and utility. The learned differentially private 1-SVM models can be released for others to perform anomaly detection without violating the privacy of individuals who participated in the training dataset.
Author: Markus Christen Publisher: Springer Nature ISBN: 3030290530 Category : Philosophy Languages : en Pages : 388
Book Description
This open access book provides the first comprehensive collection of papers that provide an integrative view on cybersecurity. It discusses theories, problems and solutions on the relevant ethical issues involved. This work is sorely needed in a world where cybersecurity has become indispensable to protect trust and confidence in the digital infrastructure whilst respecting fundamental values like equality, fairness, freedom, or privacy. The book has a strong practical focus as it includes case studies outlining ethical issues in cybersecurity and presenting guidelines and other measures to tackle those issues. It is thus not only relevant for academics but also for practitioners in cybersecurity such as providers of security software, governmental CERTs or Chief Security Officers in companies.
Author: Friedrich L. Bauer Publisher: IOS Press ISBN: 9781586030155 Category : Computers Languages : en Pages : 346
Book Description
The final quarter of the 20th century has seen the establishment of a global computational infrastructure. This and the advent of programming languages such as Java, supporting mobile distributed computing, has posed a significant challenge to computer sciences. The infrastructure can support commerce, medicine and government, but only if communications and computing can be secured against catastrophic failure and malicious interference.
Author: Andrew Senior Publisher: Springer Science & Business Media ISBN: 1848823010 Category : Computers Languages : en Pages : 213
Book Description
Protecting Privacy in Video Surveillance offers the state of the art from leading researchers and experts in the field. This broad ranging volume discusses the topic from various technical points of view and also examines surveillance from a societal perspective. A comprehensive introduction carefully guides the reader through the collection of cutting-edge research and current thinking. The technical elements of the field feature topics from MERL blind vision, stealth vision and privacy by de-identifying face images, to using mobile communications to assert privacy from video surveillance, and using wearable computing devices for data collection in surveillance environments. Surveillance and society is approached with discussions of security versus privacy, the rise of surveillance, and focusing on social control. This rich array of the current research in the field will be an invaluable reference for researchers, as well as graduate students.
Author: Kristin Lauter
Author: Kristin Lauter
Author: Xun Yi
Author: Sonam Mittal
Author: Yehuda Lindell
Author: Ayantika Chatterjee
Author: Suxin Guo
Author: Markus Christen
Author: Friedrich L. Bauer
Author: Andrew Senior
Author: Tianqing Zhu