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The Algorithmic Foundations of Differential Privacy

The Algorithmic Foundations of Differential Privacy PDF Author: Cynthia Dwork
Publisher:
ISBN: 9781601988188
Category : Computers
Languages : en
Pages : 286

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Book Description
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.

The Algorithmic Foundations of Differential Privacy

The Algorithmic Foundations of Differential Privacy PDF Author: Cynthia Dwork
Publisher:
ISBN: 9781601988188
Category : Computers
Languages : en
Pages : 286

View

Book Description
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 in Statistical Databases

Privacy in Statistical Databases PDF Author: Josep Domingo-Ferrer
Publisher: Springer Science & Business Media
ISBN: 3642158374
Category : Computers
Languages : en
Pages : 297

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Book Description
This book constitutes the proceedings of the International Conference on Privacy in Statistical Databases held in Corfu, Greece, in September 2010.

Machine Learning and Knowledge Extraction

Machine Learning and Knowledge Extraction PDF Author: Andreas Holzinger
Publisher: Springer Nature
ISBN: 3030297268
Category : Computers
Languages : en
Pages : 416

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Book Description
This book constitutes the refereed proceedings of the IFIP TC 5, TC 12, WG 8.4, 8.9, 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2019, held in Canterbury, UK, in August 2019. The 25 revised full papers presented were carefully reviewed and selected from 45 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity.

Resilience Engineering

Resilience Engineering PDF Author: Nii Attoh-Okine
Publisher: Cambridge University Press
ISBN: 0521193494
Category : Mathematics
Languages : en
Pages : 202

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Book Description
The book is intended for readers who have backgrounds in probability. It is suitable for practicing engineers, analysts, and researchers.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases PDF Author: Annalisa Appice
Publisher: Springer
ISBN: 3319235257
Category : Computers
Languages : en
Pages : 773

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Book Description
The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

New Trends in Database and Information Systems

New Trends in Database and Information Systems PDF Author: Silvia Chiusano
Publisher: Springer Nature
ISBN: 3031157435
Category : Database management
Languages : en
Pages : 675

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Book Description
This book constitutes the proceedings of the 26th European Conference on Advances in Databases and Information Systems, ADBIS 2022, held in Turin, Italy, in September 2022. The 29 short papers presented were carefully reviewed and selected from 90 submissions. The selected short papers are organized in the following sections: data understanding, modeling and visualization; fairness in data processing; data understanding, modeling and visualization; data access optimization; data pre-processing and cleaning; data science and machine learning. Further, papers from the following workshops and satellite events are provided in the volume: DOING: 3rd Workshop on Intelligent Data : From Data toKnowledg; K-GALS: 1st Workshop on Knowledge Graphs Analysis ona Large Scale; MADEISD: 4th Workshop on Modern Approaches in DataEngineering and Information System Design; MegaData: 2nd Workshop on Advanced Data Systems Management, Engineering, and Analytics; SWODCH: 2nd Workshop on Semantic Web and OntologyDesign for Cultural Heritage; Doctoral Consortium.

Differential Privacy for Databases

Differential Privacy for Databases PDF Author: Joseph P Near
Publisher:
ISBN: 9781680838503
Category :
Languages : en
Pages :

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Book Description
This book provides a database researcher or designer a complete, yet concise, overview of differential privacy and its deployment in database systems.

Data Privacy: Foundations, New Developments and the Big Data Challenge

Data Privacy: Foundations, New Developments and the Big Data Challenge PDF Author: Vicenç Torra
Publisher: Springer
ISBN: 3319573586
Category : Technology & Engineering
Languages : en
Pages : 269

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Book Description
This book offers a broad, cohesive overview of the field of data privacy. It discusses, from a technological perspective, the problems and solutions of the three main communities working on data privacy: statistical disclosure control (those with a statistical background), privacy-preserving data mining (those working with data bases and data mining), and privacy-enhancing technologies (those involved in communications and security) communities. Presenting different approaches, the book describes alternative privacy models and disclosure risk measures as well as data protection procedures for respondent, holder and user privacy. It also discusses specific data privacy problems and solutions for readers who need to deal with big data.

Privacy, Security, and Trust in KDD

Privacy, Security, and Trust in KDD PDF Author: Francesco Bonchi
Publisher: Springer Science & Business Media
ISBN: 3540784772
Category : Business & Economics
Languages : en
Pages : 171

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Book Description
without sacri?cing the privacy and security of the individuals to whom the data correspond.

Handbook of Mobile Data Privacy

Handbook of Mobile Data Privacy PDF Author: Aris Gkoulalas-Divanis
Publisher: Springer
ISBN: 3319981617
Category : Computers
Languages : en
Pages : 403

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Book Description
This handbook covers the fundamental principles and theory, and the state-of-the-art research, systems and applications, in the area of mobility data privacy. It is primarily addressed to computer science and statistics researchers and educators, who are interested in topics related to mobility privacy. This handbook will also be valuable to industry developers, as it explains the state-of-the-art algorithms for offering privacy. By discussing a wide range of privacy techniques, providing in-depth coverage of the most important ones, and highlighting promising avenues for future research, this handbook also aims at attracting computer science and statistics students to this interesting field of research. The advances in mobile devices and positioning technologies, together with the progress in spatiotemporal database research, have made possible the tracking of mobile devices (and their human companions) at very high accuracy, while supporting the efficient storage of mobility data in data warehouses, which this handbook illustrates. This has provided the means to collect, store and process mobility data of an unprecedented quantity, quality and timeliness. As ubiquitous computing pervades our society, user mobility data represents a very useful but also extremely sensitive source of information. On one hand, the movement traces that are left behind by the mobile devices of the users can be very useful in a wide spectrum of applications such as urban planning, traffic engineering, and environmental pollution management. On the other hand, the disclosure of mobility data to third parties may severely jeopardize the privacy of the users whose movement is recorded, leading to abuse scenarios such as user tailing and profiling. A significant amount of research work has been conducted in the last 15 years in the area of mobility data privacy and important research directions, such as privacy-preserving mobility data management, privacy in location sensing technologies and location-based services, privacy in vehicular communication networks, privacy in location-based social networks, privacy in participatory sensing systems which this handbook addresses.. This handbook also identifies important privacy gaps in the use of mobility data and has resulted to the adoption of international laws for location privacy protection (e.g., in EU, US, Canada, Australia, New Zealand, Japan, Singapore), as well as to a large number of interesting technologies for privacy-protecting mobility data, some of which have been made available through open-source systems and featured in real-world applications.