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Preserving Privacy In On Line Analytical Processing Olap
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Book Synopsis Preserving Privacy in On-Line Analytical Processing (OLAP) by : Lingyu Wang
Download or read book Preserving Privacy in On-Line Analytical Processing (OLAP) written by Lingyu Wang and published by Springer Science & Business Media. This book was released on 2007-04-06 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. OLAP systems usually need to meet two conflicting goals. First, the sensitive data stored in underlying data warehouses must be kept secret. Second, analytical queries about the data must be allowed for decision support purposes. The main challenge is that sensitive data can be inferred from answers to seemingly innocent aggregations of the data. This volume reviews a series of methods that can precisely answer data cube-style OLAP, regarding sensitive data while provably preventing adversaries from inferring data.
Book Synopsis Preserving Privacy in On-line Analytical Processing Data Cubes by : Lingyu Wang
Download or read book Preserving Privacy in On-line Analytical Processing Data Cubes written by Lingyu Wang and published by . This book was released on 2006 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Privacy-Preserving Machine Learning by : J. Morris Chang
Download or read book Privacy-Preserving Machine Learning written by J. Morris Chang and published by Simon and Schuster. This book was released on 2023-05-23 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy-Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You’ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. By the time you’re done reading, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance. About the Technology Machine learning applications need massive amounts of data. It’s up to you to keep the sensitive information in those data sets private and secure. Privacy preservation happens at every point in the ML process, from data collection and ingestion to model development and deployment. This practical book teaches you the skills you’ll need to secure your data pipelines end to end. About the Book Privacy-Preserving Machine Learning explores privacy preservation techniques through real-world use cases in facial recognition, cloud data storage, and more. You’ll learn about practical implementations you can deploy now, future privacy challenges, and how to adapt existing technologies to your needs. Your new skills build towards a complete security data platform project you’ll develop in the final chapter. What’s Inside Differential and compressive privacy techniques Privacy for frequency or mean estimation, naive Bayes classifier, and deep learning Privacy-preserving synthetic data generation Enhanced privacy for data mining and database applications About the Reader For machine learning engineers and developers. Examples in Python and Java. About the Author J. Morris Chang is a professor at the University of South Florida. His research projects have been funded by DARPA and the DoD. Di Zhuang is a security engineer at Snap Inc. Dumindu Samaraweera is an assistant research professor at the University of South Florida. The technical editor for this book, Wilko Henecka, is a senior software engineer at Ambiata where he builds privacy-preserving software. Table of Contents PART 1 - BASICS OF PRIVACY-PRESERVING MACHINE LEARNING WITH DIFFERENTIAL PRIVACY 1 Privacy considerations in machine learning 2 Differential privacy for machine learning 3 Advanced concepts of differential privacy for machine learning PART 2 - LOCAL DIFFERENTIAL PRIVACY AND SYNTHETIC DATA GENERATION 4 Local differential privacy for machine learning 5 Advanced LDP mechanisms for machine learning 6 Privacy-preserving synthetic data generation PART 3 - BUILDING PRIVACY-ASSURED MACHINE LEARNING APPLICATIONS 7 Privacy-preserving data mining techniques 8 Privacy-preserving data management and operations 9 Compressive privacy for machine learning 10 Putting it all together: Designing a privacy-enhanced platform (DataHub)
Book Synopsis Intrusion Detection Systems by : Roberto Di Pietro
Download or read book Intrusion Detection Systems written by Roberto Di Pietro and published by Springer Science & Business Media. This book was released on 2008-06-12 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: To defend against computer and network attacks, multiple, complementary security devices such as intrusion detection systems (IDSs), and firewalls are widely deployed to monitor networks and hosts. These various IDSs will flag alerts when suspicious events are observed. This book is an edited volume by world class leaders within computer network and information security presented in an easy-to-follow style. It introduces defense alert systems against computer and network attacks. It also covers integrating intrusion alerts within security policy framework for intrusion response, related case studies and much more.
Book Synopsis Encyclopedia of Business Analytics and Optimization by : Wang, John
Download or read book Encyclopedia of Business Analytics and Optimization written by Wang, John and published by IGI Global. This book was released on 2014-02-28 with total page 2862 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.
Download or read book Big Data written by Kuan-Ching Li and published by CRC Press. This book was released on 2015-02-23 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: As today's organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages.Pre
Book Synopsis Secure Data Management in Decentralized Systems by : Ting Yu
Download or read book Secure Data Management in Decentralized Systems written by Ting Yu and published by Springer Science & Business Media. This book was released on 2007-05-11 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of database security has expanded greatly, with the rapid development of global inter-networked infrastructure. Databases are no longer stand-alone systems accessible only to internal users of organizations. Today, businesses must allow selective access from different security domains. New data services emerge every day, bringing complex challenges to those whose job is to protect data security. The Internet and the web offer means for collecting and sharing data with unprecedented flexibility and convenience, presenting threats and challenges of their own. This book identifies and addresses these new challenges and more, offering solid advice for practitioners and researchers in industry.
Book Synopsis Novel Algorithms and Techniques in Telecommunications and Networking by : Tarek Sobh
Download or read book Novel Algorithms and Techniques in Telecommunications and Networking written by Tarek Sobh and published by Springer Science & Business Media. This book was released on 2010-01-30 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Novel Algorithms and Techniques in Telecommunications and Networking includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Industrial Electronics, Technology and Automation, Telecommunications and Networking. Novel Algorithms and Techniques in Telecommunications and Networking includes selected papers form the conference proceedings of the International Conference on Telecommunications and Networking (TeNe 08) which was part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2008).
Book Synopsis Data Warehousing and Knowledge Discovery by : A Min Tjoa
Download or read book Data Warehousing and Knowledge Discovery written by A Min Tjoa and published by Springer Science & Business Media. This book was released on 2005-08-17 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2005, held in Copenhagen, Denmark, in August 2005. The 51 revised full papers presented were carefully reviewed and selected from 196 submissions. The papers are organized in topical sections on data warehouses, evaluation and tools, schema transformations, materialized views, aggregates, data warehouse queries and database processing issues, data mining algorithms and techniques, association rules, text processing and classification, security and privacy issues, patterns, and cluster and classification.
Book Synopsis Database Technologies: Concepts, Methodologies, Tools, and Applications by : Erickson, John
Download or read book Database Technologies: Concepts, Methodologies, Tools, and Applications written by Erickson, John and published by IGI Global. This book was released on 2009-02-28 with total page 2962 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This reference expands the field of database technologies through four-volumes of in-depth, advanced research articles from nearly 300 of the world's leading professionals"--Provided by publisher.
Book Synopsis Algorithms and Architectures for Data Privacy by : Dilys Thomas
Download or read book Algorithms and Architectures for Data Privacy written by Dilys Thomas and published by . This book was released on 2007 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Secure Data Management by : Willem Jonker
Download or read book Secure Data Management written by Willem Jonker and published by Springer Science & Business Media. This book was released on 2010-09 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 7th VLDB Workshop on Secure Data Management held in Singapore in September 17, 2010. The regular papers in the proceedings are grouped into two sections. The first section focuses on privacy. The papers in this section present a balanced mix of theoretical work on anonymity and application-oriented work. The second focuses on data security in open environments. The papers address issues related to the management of confidential data that is stored in or released to open environments, such as, for example, in cloud computing.
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.
Book Synopsis Privacy in Statistical Databases by : Josep Domingo-Ferrer
Download or read book Privacy in Statistical Databases written by Josep Domingo-Ferrer and published by Springer Nature. This book was released on 2022-09-14 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2022, held in Paris, France, during September 21-23, 2022. The 25 papers presented in this volume were carefully reviewed and selected from 45 submissions. They were organized in topical sections as follows: Privacy models; tabular data; disclosure risk assessment and record linkage; privacy-preserving protocols; unstructured and mobility data; synthetic data; machine learning and privacy; and case studies.
Download or read book Middleware 2005 written by Gustavo Alonso and published by Springer. This book was released on 2005-11-19 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the ACM/IFIP/USENIX International Conference on Distributed Systems Platforms, Middleware 2005, held in Grenoble, France in November/December 2005. The 18 revised full papers and 6 short papers presented were carefully reviewed and selected from 112 submissions. The papers are organized in topical sections on security and privacy, peer-to-peer computing, XML and service discovery, distribution and real time processing, publish/subscribe systems and content distribution, and middleware architecture.
Book Synopsis Encyclopedia of data warehousing and mining by : John Wang
Download or read book Encyclopedia of data warehousing and mining written by John Wang and published by . This book was released on 2008 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Contributions to Data Privacy in Cloud Data Warehouses by : Somayeh Sobati Moghadam
Download or read book Contributions to Data Privacy in Cloud Data Warehouses written by Somayeh Sobati Moghadam and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, data outsourcing scenarios are ever more common with the advent of cloud computing. Cloud computing appeals businesses and organizations because of a wide variety of benefits such as cost savings and service benefits. Moreover, cloud computing provides higher availability, scalability, and more effective disaster recovery rather than in-house operations. One of the most notable cloud outsourcing services is database outsourcing (Database-as-a-Service), where individuals and organizations outsource data storage and management to a Cloud Service Provider (CSP). Naturally, such services allow storing a data warehouse (DW) on a remote, untrusted CSP and running on-line analytical processing (OLAP).Although cloud data outsourcing induces many benefits, it also brings out security and in particular privacy concerns. A typical solution to preserve data privacy is encrypting data locally before sending them to an external server. Secure database management systems use various encryption schemes, but they either induce computational and storage overhead or reveal some information about data, which jeopardizes privacy.In this thesis, we propose a new secure secret splitting scheme (S4) inspired by Shamir's secret sharing. S4 implements an additive homomorphic scheme, i.e., additions can be directly computed over encrypted data. S4 addresses the shortcomings of existing approaches by reducing storage and computational overhead while still enforcing a reasonable level of privacy. S4 is efficient both in terms of storage and computing, which is ideal for data outsourcing scenarios that consider the user has limited computation and storage resources. Experimental results confirm the efficiency of S4 in terms of computation and storage overhead with respect to existing solutions.Moreover, we also present new order-preserving schemes, order-preserving indexing (OPI) and wrap-around order-preserving indexing (waOPI), which are practical on cloud outsourced DWs. We focus on the problem of performing range and exact match queries over encrypted data. In contrast to existing solutions, our schemes prevent performing statistical and frequency analysis by an adversary. While providing data privacy, the proposed schemes bear good performance and lead to minimal change for existing software.
