Preserving Privacy in On-Line Analytical Processing (OLAP)

Preserving Privacy in On-Line Analytical Processing (OLAP) PDF Author: Lingyu Wang
Publisher: Springer Science & Business Media
ISBN: 0387462740
Category : Computers
Languages : en
Pages : 180

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Book Description
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.

Preserving Privacy in On-Line Analytical Processing (OLAP)

Preserving Privacy in On-Line Analytical Processing (OLAP) PDF Author: Lingyu Wang
Publisher: Springer Science & Business Media
ISBN: 0387462740
Category : Computers
Languages : en
Pages : 180

Get Book Here

Book Description
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.

Intrusion Detection Systems

Intrusion Detection Systems PDF Author: Roberto Di Pietro
Publisher: Springer Science & Business Media
ISBN: 0387772669
Category : Computers
Languages : en
Pages : 265

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Book Description
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.

Encyclopedia of Business Analytics and Optimization

Encyclopedia of Business Analytics and Optimization PDF Author: Wang, John
Publisher: IGI Global
ISBN: 1466652039
Category : Business & Economics
Languages : en
Pages : 2862

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Book Description
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.

Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning PDF Author: J. Morris Chang
Publisher: Simon and Schuster
ISBN: 1638352755
Category : Computers
Languages : en
Pages : 334

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Book Description
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)

Big Data

Big Data PDF Author: Kuan-Ching Li
Publisher: CRC Press
ISBN: 1482240564
Category : Computers
Languages : en
Pages : 478

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Book Description
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

Secure Data Management in Decentralized Systems

Secure Data Management in Decentralized Systems PDF Author: Ting Yu
Publisher: Springer Science & Business Media
ISBN: 0387276963
Category : Computers
Languages : en
Pages : 461

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Book Description
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.

Novel Algorithms and Techniques in Telecommunications and Networking

Novel Algorithms and Techniques in Telecommunications and Networking PDF Author: Tarek Sobh
Publisher: Springer Science & Business Media
ISBN: 9048136628
Category : Technology & Engineering
Languages : en
Pages : 502

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Book Description
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).

Privacy in Statistical Databases

Privacy in Statistical Databases PDF Author: Josep Domingo-Ferrer
Publisher: Springer Nature
ISBN: 3031139453
Category : Computers
Languages : en
Pages : 375

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Book Description
​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.

Data Warehousing and Knowledge Discovery

Data Warehousing and Knowledge Discovery PDF Author: A Min Tjoa
Publisher: Springer Science & Business Media
ISBN: 354028558X
Category : Business & Economics
Languages : en
Pages : 551

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Book Description
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.

Database Technologies: Concepts, Methodologies, Tools, and Applications

Database Technologies: Concepts, Methodologies, Tools, and Applications PDF Author: Erickson, John
Publisher: IGI Global
ISBN: 1605660590
Category : Business & Economics
Languages : en
Pages : 2962

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Book Description
"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.