Advertisement
| calibrating noise to sensitivity in private data analysis: Theory of Cryptography , 2006 |
| calibrating noise to sensitivity in private data analysis: Advances in Cryptology - CRYPTO 2008 David Wagner, 2008-08-21 This book constitutes the refereed proceedings of the 28th Annual International Cryptology Conference, CRYPTO 2008, held in Santa Barbara, CA, USA in August 2008. The 32 revised full papers presented were carefully reviewed and selected from 184 submissions. Addressing all current foundational, theoretical and research aspects of cryptology, cryptography, and cryptanalysis as well as advanced applications, the papers are organized in topical sections on random oracles, applications, public-key crypto, hash functions, cryptanalysis, multiparty computation, privacy, zero knowledge, and oblivious transfer. |
| calibrating noise to sensitivity in private data analysis: Theory of Cryptography Shai Halevi, Tal Rabin, 2006-03-01 This book constitutes the refereed proceedings of the Third Theory of Cryptography Conference, TCC 2006, held in March 2006. The 31 revised full papers presented were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on zero-knowledge, primitives, assumptions and models, the bounded-retrieval model, privacy, secret sharing and multi-party computation, universally-composible security, one-way functions and friends, and pseudo-random functions and encryption. |
| calibrating noise to sensitivity in private data analysis: Hands-On Differential Privacy Ethan Cowan, Michael Shoemate, Mayana Pereira, 2024-05-16 Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows. With this book, you'll learn: How DP guarantees privacy when other data anonymization methods don't What preserving individual privacy in a dataset entails How to apply DP in several real-world scenarios and datasets Potential privacy attack methods, including what it means to perform a reidentification attack How to use the OpenDP library in privacy-preserving data releases How to interpret guarantees provided by specific DP data releases |
| calibrating noise to sensitivity in private data analysis: The Algorithmic Foundations of Differential Privacy Cynthia Dwork, Aaron Roth, 2014 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. |
| calibrating noise to sensitivity in private data analysis: Privacy in Statistical Databases Josep Domingo-Ferrer, 2014-09-10 This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2014, held in Ibiza, Spain in September 2014 under the sponsorship of the UNESCO chair in Data Privacy. The 27 revised full papers presented were carefully reviewed and selected from 41 submissions. The scope of the conference is on following topics: tabular data protection, microdata masking, protection using privacy models, synthetic data, record linkage, remote access, privacy-preserving protocols, and case studies. |
| calibrating noise to sensitivity in private data analysis: Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications Tran Khanh Dang, Josef Küng, Makoto Takizawa, Tai M. Chung, 2020-11-19 This book constitutes the proceedings of the 7th International Conference on Future Data and Security Engineering, FDSE 2020, held in Quy Nhon, Vietnam, in November 2020.* The 29 full papers and 8 short were carefully reviewed and selected from 161 submissions. The selected papers are organized into the following topical headings: big data analytics and distributed systems; security and privacy engineering; industry 4.0 and smart city: data analytics and security; data analytics and healthcare systems; machine learning-based big data processing; emerging data management systems and applications; and short papers: security and data engineering. * The conference was held virtually due to the COVID-19 pandemic. |
| calibrating noise to sensitivity in private data analysis: Privacy in a Digital, Networked World Sherali Zeadally, Mohamad Badra, 2015-10-13 This comprehensive textbook/reference presents a focused review of the state of the art in privacy research, encompassing a range of diverse topics. The first book of its kind designed specifically to cater to courses on privacy, this authoritative volume provides technical, legal, and ethical perspectives on privacy issues from a global selection of renowned experts. Features: examines privacy issues relating to databases, P2P networks, big data technologies, social networks, and digital information networks; describes the challenges of addressing privacy concerns in various areas; reviews topics of privacy in electronic health systems, smart grid technology, vehicular ad-hoc networks, mobile devices, location-based systems, and crowdsourcing platforms; investigates approaches for protecting privacy in cloud applications; discusses the regulation of personal information disclosure and the privacy of individuals; presents the tools and the evidence to better understand consumers’ privacy behaviors. |
| calibrating noise to sensitivity in private data analysis: Algorithms for Data and Computation Privacy Alex X. Liu, Rui Li, 2020-11-28 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. |
| calibrating noise to sensitivity in private data analysis: Applied Mathematics, Modeling and Computer Simulation C.-H. Chen, 2022-02-25 The pervasiveness of computers in every field of science, industry and everyday life has meant that applied mathematics, particularly in relation to modeling and simulation, has become ever more important in recent years. This book presents the proceedings of the 2021 International Conference on Applied Mathematics, Modeling and Computer Simulation (AMMCS 2021), hosted in Wuhan, China, and held as a virtual event from 13 to 14 November 2021. The aim of the conference is to foster the knowledge and understanding of recent advances across the broad fields of applied mathematics, modeling and computer simulation, and it provides an annual platform for scholars and researchers to communicate important recent developments in their areas of specialization to colleagues and other scientists in related disciplines. This year more than 150 participants were able to exchange knowledge and discuss recent developments via the conference. The book contains 115 peer-reviewed papers, selected from more than 250 submissions and ranging from the theoretical and conceptual to the strongly pragmatic and all addressing industrial best practice. Topics covered include mathematical modeling and applications, engineering applications and scientific computations, and the simulation of intelligent systems. Providing an overview of recent development and with a mix of practical experiences and enlightening ideas, the book will be of interest to researchers and practitioners everywhere. |
| calibrating noise to sensitivity in private data analysis: Medical Data Privacy Handbook Aris Gkoulalas-Divanis, Grigorios Loukides, 2015-11-26 This handbook covers Electronic Medical Record (EMR) systems, which enable the storage, management, and sharing of massive amounts of demographic, diagnosis, medication, and genomic information. It presents privacy-preserving methods for medical data, ranging from laboratory test results to doctors’ comments. The reuse of EMR data can greatly benefit medical science and practice, but must be performed in a privacy-preserving way according to data sharing policies and regulations. Written by world-renowned leaders in this field, each chapter offers a survey of a research direction or a solution to problems in established and emerging research areas. The authors explore scenarios and techniques for facilitating the anonymization of different types of medical data, as well as various data mining tasks. Other chapters present methods for emerging data privacy applications and medical text de-identification, including detailed surveys of deployed systems. A part of the book is devoted to legislative and policy issues, reporting on the US and EU privacy legislation and the cost of privacy breaches in the healthcare domain. This reference is intended for professionals, researchers and advanced-level students interested in safeguarding medical data. |
| calibrating noise to sensitivity in private data analysis: Database Systems for Advanced Applications Selçuk Candan, Lei Chen, Torben Bach Pedersen, Lijun Chang, Wen Hua, 2017-03-20 This two volume set LNCS 10177 and 10178 constitutes the refereed proceedings of the 22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017, held in Suzhou, China, in March 2017. The 73 full papers, 9 industry papers, 4 demo papers and 3 tutorials were carefully selected from a total of 300 submissions. The papers are organized around the following topics: semantic web and knowledge management; indexing and distributed systems; network embedding; trajectory and time series data processing; data mining; query processing and optimization; text mining; recommendation; security, privacy, senor and cloud; social network analytics; map matching and spatial keywords; query processing and optimization; search and information retrieval; string and sequence processing; stream date processing; graph and network data processing; spatial databases; real time data processing; big data; social networks and graphs. |
| calibrating noise to sensitivity in private data analysis: Privacy Enhancing Technologies Simone Fischer-Hübner, Matthew Wright, 2012-06-28 This book constitutes the refereed proceedings of the 12 th International Symposium on Privacy Enhancing Technologies, PET 2012, held in Vigo, Spain, in July 2012. The 16 full papers presented were carefully selected from 72 submissions. Topics addressed include anonymization of statistics, content, and traffic, network traffic analysis, censorship-resistant systems, user profiling, training users in privacy risk management, and privacy of internet and cloud-bases services. A further highlight is the HotPETS session, designed as a venue to present existing but still preliminary and evolving ideas. |
| calibrating noise to sensitivity in private data analysis: Data Privacy Management, and Security Assurance Joaquin Garcia-Alfaro, Guillermo Navarro-Arribas, Alessandro Aldini, Fabio Martinelli, Neeraj Suri, 2016-02-22 This book constitutes the revised selected papers of the 10th International Workshop on Data Privacy Management, DPM 2015, and the 4th International Workshop on Quantitative Aspects in Security Assurance, QASA 2015, held in Vienna, Austria, in September 2015, co-located with the 20th European Symposium on Research in Computer Security, ESORICS 2015. In the DPM 2015 workshop edition, 39 submissions were received. In the end, 8 full papers, accompanied by 6 short papers, 2 position papers and 1 keynote were presented in this volume. The QASA workshop series responds to the increasing demand for techniques to deal with quantitative aspects of security assurance at several levels of the development life-cycle of systems and services, from requirements elicitation to run-time operation and maintenance. QASA 2015 received 11 submissions, of which 4 papers are presented in this volume as well. |
| calibrating noise to sensitivity in private data analysis: Computational Science – ICCS 2020 Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, João Teixeira, 2020-06-18 The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic. |
| calibrating noise to sensitivity in private data analysis: Web Information Systems and Applications Chunxiao Xing, Xiaoming Fu, Yong Zhang, Guigang Zhang, Chaolemen Borjigin, 2021-09-16 This book constitutes the proceedings of the 18th International Conference on Web Information Systems and Applications, WISA 2021, held in Kaifeng, China, in September 2021. The 49 full papers and 18 short papers presented were carefully reviewed and selected from 206 submissions. The papers are grouped in topical sections on world wide web, query processing and algorithm, natural language processing, machine learning, data mining, data privacy and security. |
| calibrating noise to sensitivity in private data analysis: Security and Privacy in New Computing Environments Wenbo Shi, Xiaofeng Chen, Kim-Kwang Raymond Choo, 2022-03-12 This book constitutes the refereed proceedings of the 4thInternational Conference on Security and Privacy in New Computing Environments, SPNCE 2021, held in December 2021. Due to COVID-19 pandemic the conference was held virtually. The 33 full papers were selected from 61 submissions and focus on security and privacy in new computing environments. The theme of SPNCE 2021 was “Secure Wireless Communication Systems: Infrastructure, Algorithms, and Management”. |
| calibrating noise to sensitivity in private data analysis: Algorithms –- ESA 2012 Leah Epstein, Paolo Ferragina, 2012-08-30 This book constitutes the refereed proceedings of the 20th Annual European Symposium on Algorithms, ESA 2012, held in Ljubljana, Slovenia, in September 2012 in the context of the combined conference ALGO 2012. The 69 revised full papers presented were carefully reviewed and selected from 285 initial submissions: 56 out of 231 in track design and analysis and 13 out of 54 in track engineering and applications. The papers are organized in topical sections such as algorithm engineering; algorithmic aspects of networks; algorithmic game theory; approximation algorithms; computational biology; computational finance; computational geometry; combinatorial optimization; data compression; data structures; databases and information retrieval; distributed and parallel computing; graph algorithms; hierarchical memories; heuristics and meta-heuristics; mathematical programming; mobile computing; on-line algorithms; parameterized complexity; pattern matching, quantum computing; randomized algorithms; scheduling and resource allocation problems; streaming algorithms. |
| calibrating noise to sensitivity in private data analysis: Database Systems for Advanced Applications Jian Pei, Yannis Manolopoulos, Shazia Sadiq, Jianxin Li, 2018-05-11 This two-volume set LNCS 10827 and LNCS 10828 constitutes the refereed proceedings of the 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, held in Gold Coast, QLD, Australia, in May 2018. The 83 full papers, 21 short papers, 6 industry papers, and 8 demo papers were carefully selected from a total of 360 submissions. The papers are organized around the following topics: network embedding; recommendation; graph and network processing; social network analytics; sequence and temporal data processing; trajectory and streaming data; RDF and knowledge graphs; text and data mining; medical data mining; security and privacy; search and information retrieval; query processing and optimizations; data quality and crowdsourcing; learning models; multimedia data processing; and distributed computing. |
| calibrating noise to sensitivity in private data analysis: Web and Internet Economics Yang Cai, Adrian Vetta, 2016-12-09 This book constitutes the thoroughly refereed proceedings of the 12th International Conference on Web and Internet Economics, WINE 2016, held in Montreal, QC, Canada, in December 2016. The 35 regular papers presented together with 3 invited talks were carefully reviewed and selected from 88 submissions. The Conference on Web and Internet Economics (WINE) is an interdisciplinary forum for the exchange of ideas and results on incentives and computation arising from the following fields: Theoretical Computer Science, Artificial Intelligence, and Microeconomics. |
| calibrating noise to sensitivity in private data analysis: AI 2015: Advances in Artificial Intelligence Bernhard Pfahringer, Jochen Renz, 2015-11-21 This book constitutes the refereed proceedings of the 28th Australasian Joint Conference on Artificial Intelligence, AI 2015, held in Canberra, Australia, in November/December 2015. The 39 full papers and 18 short papers presented were carefully reviewed and selected from 102 submissions. |
| calibrating noise to sensitivity in private data analysis: Privacy Technologies and Policy Erich Schweighofer, Herbert Leitold, Andreas Mitrakas, Kai Rannenberg, 2017-10-10 This book constitutes the thoroughly refereed post-conference proceedings of the 5th Annual Privacy Forum, APF 2017, held in Vienna, Austria, in June 2017. The 12 revised full papers were carefully selected from 41 submissions on the basis of significance, novelty, and scientific quality. These selected papers are organized in three different chapters corresponding to the conference sessions. The first chapter, “Data Protection Regulation”, discusses topics concerning big genetic data, a privacy-preserving European identity ecosystem, the right to be forgotten und the re-use of privacy risk analysis. The second chapter, “Neutralisation and Anonymization”, discusses neutralisation of threat actors, privacy by design data exchange between CSIRTs, differential privacy and database anonymization. Finally, the third chapter, “Privacy Policies in Practice”, discusses privacy by design, privacy scores, privacy data management in healthcare and trade-offs between privacy and utility. |
| calibrating noise to sensitivity in private data analysis: Advances in Cryptology - CRYPTO 2009 Shai Halevi, 2009-08-18 This book constitutes the refereed proceedings of the 29th Annual International Cryptology Conference, CRYPTO 2009, held in Santa Barbara, CA, USA in August 2009. The 38 revised full papers presented were carefully reviewed and selected from 213 submissions. Addressing all current foundational, theoretical and research aspects of cryptology, cryptography, and cryptanalysis as well as advanced applications, the papers are organized in topical sections on key leakage, hash-function cryptanalysis, privacy and anonymity, interactive proofs and zero-knowledge, block-cipher cryptanalysis, modes of operation, elliptic curves, cryptographic hardness, merkle puzzles, cryptography in the physical world, attacks on signature schemes, secret sharing and secure computation, cryptography and game-theory, cryptography and lattices, identity-based encryption and cryptographers’ toolbox. |
| calibrating noise to sensitivity in private data analysis: Information Security and Privacy Joseph K. Liu, Ron Steinfeld, 2016-06-29 The two-volume set LNCS 9722 and LNCS 9723 constitutes the refereed proceedings of the 21st Australasian Conference on Information Security and Privacy, ACISP 2016, held in Melbourne, VIC, Australia, in July 2016. The 52 revised full and 8 short papers presented together with 6 invited papers in this double volume were carefully revised and selected from 176 submissions. The papers of Part I (LNCS 9722) are organized in topical sections on National Security Infrastructure; Social Network Security; Bitcoin Security; Statistical Privacy; Network Security; Smart City Security; Digital Forensics; Lightweight Security; Secure Batch Processing; Pseudo Random/One-Way Function; Cloud Storage Security; Password/QR Code Security; and Functional Encryption and Attribute-Based Cryptosystem. Part II (LNCS 9723) comprises topics such as Signature and Key Management; Public Key and Identity-Based Encryption; Searchable Encryption; Broadcast Encryption; Mathematical Primitives; Symmetric Cipher; Public Key and Identity-Based Encryption; Biometric Security; Digital Forensics; National Security Infrastructure; Mobile Security; Network Security; and Pseudo Random/One-Way Function. |
| calibrating noise to sensitivity in private data analysis: Principles of Security and Trust Pierpaolo Degano, Joshua D. Guttman, 2012-03-22 This book constitutes the refereed proceedings of the first International Conference on Principles of Security and Trust, POST 2012, held in Tallinn, Estonia, in March/April 2012, as part of ETAPS 2012, the European Joint Conferences on Theory and Practice of Software. The 20 papers, presented together with the abstract of an invited talk and a joint-ETAPS paper, were selected from a total of 67 submissions. Topics covered by the papers include: foundations of security, authentication, confidentiality, privacy and anonymity, authorization and trust, network security, protocols for security, language-based security, and quantitative security properties. |
| calibrating noise to sensitivity in private data analysis: Advanced Data Mining and Applications Guojun Gan, Bohan Li, Xue Li, Shuliang Wang, 2018-12-28 This book constitutes the refereed proceedings of the 14th International Conference on Advanced Data Mining and Applications, ADMA 2018, held in Nanjing, China in November 2018. The 23 full and 22 short papers presented in this volume were carefully reviewed and selected from 104 submissions. The papers were organized in topical sections named: Data Mining Foundations; Big Data; Text and Multimedia Mining; Miscellaneous Topics. |
| calibrating noise to sensitivity in private data analysis: Personal Analytics and Privacy. An Individual and Collective Perspective Riccardo Guidotti, Anna Monreale, Dino Pedreschi, Serge Abiteboul, 2017-12-04 This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Personal Analytics and Privacy, PAP 2017, held in Skopje, Macedonia, in September 2017. The 14 papers presented together with 2 invited talks in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as personal analytics, personal data mining and privacy in the context where real individual data are used for developing a data-driven service, for realizing a social study aimed at understanding nowadays society, and for publication purposes. |
| calibrating noise to sensitivity in private data analysis: Theory of Cryptography Rafael Pass, Krzysztof Pietrzak, 2020-12-12 This three-volume set, LNCS 12550, 12551, and 12552, constitutes the refereed proceedings of the 18th International Conference on Theory of Cryptography, TCCC 2020, held in Durham, NC, USA, in November 2020. The total of 71 full papers presented in this three-volume set was carefully reviewed and selected from 167 submissions. Amongst others they cover the following topics: study of known paradigms, approaches, and techniques, directed towards their better understanding and utilization; discovery of new paradigms, approaches and techniques that overcome limitations of the existing ones, formulation and treatment of new cryptographic problems; study of notions of security and relations among them; modeling and analysis of cryptographic algorithms; and study of the complexity assumptions used in cryptography. Due to the Corona pandemic this event was held virtually. |
| calibrating noise to sensitivity in private data analysis: Foundations and Practice of Security Guy-Vincent Jourdan, Laurent Mounier, Carlisle Adams, Florence Sèdes, Joaquin Garcia-Alfaro, 2023-03-31 This book constitutes the refereed proceedings of the 15th International Symposium on Foundations and Practice of Security, FPS 2022, held in Ottawa, ON, Canada, during December 12–14, 2022. The 26 regular and 3 short papers presented in this book were carefully reviewed and selected from 83 submissions. The papers have been organized in the following topical sections: Cryptography; Machine Learning; Cybercrime and Privacy; Physical-layer Security; Blockchain; IoT and Security Protocols; and Short Papers. |
| calibrating noise to sensitivity in private data analysis: Statistical Confidentiality George T. Duncan, Mark Elliot, Gonzalez Juan Jose Salazar, 2011-03-22 Because statistical confidentiality embraces the responsibility for both protecting data and ensuring its beneficial use for statistical purposes, those working with personal and proprietary data can benefit from the principles and practices this book presents. Researchers can understand why an agency holding statistical data does not respond well to the demand, “Just give me the data; I’m only going to do good things with it.” Statisticians can incorporate the requirements of statistical confidentiality into their methodologies for data collection and analysis. Data stewards, caught between those eager for data and those who worry about confidentiality, can use the tools of statistical confidentiality toward satisfying both groups. The eight chapters lay out the dilemma of data stewardship organizations (such as statistical agencies) in resolving the tension between protecting data from snoopers while providing data to legitimate users, explain disclosure risk and explore the types of attack that a data snooper might mount, present the methods of disclosure risk assessment, give techniques for statistical disclosure limitation of both tabular data and microdata, identify measures of the impact of disclosure limitation on data utility, provide restricted access methods as administrative procedures for disclosure control, and finally explore the future of statistical confidentiality. |
| calibrating noise to sensitivity in private data analysis: Information Technology Security Debasis Gountia, |
| calibrating noise to sensitivity in private data analysis: Encyclopedia of Cryptography and Security Henk C.A. van Tilborg, Sushil Jajodia, 2014-07-08 Expanded into two volumes, the Second Edition of Springer’s Encyclopedia of Cryptography and Security brings the latest and most comprehensive coverage of the topic: Definitive information on cryptography and information security from highly regarded researchers Effective tool for professionals in many fields and researchers of all levels Extensive resource with more than 700 contributions in Second Edition 5643 references, more than twice the number of references that appear in the First Edition With over 300 new entries, appearing in an A-Z format, the Encyclopedia of Cryptography and Security provides easy, intuitive access to information on all aspects of cryptography and security. As a critical enhancement to the First Edition’s base of 464 entries, the information in the Encyclopedia is relevant for researchers and professionals alike. Topics for this comprehensive reference were elected, written, and peer-reviewed by a pool of distinguished researchers in the field. The Second Edition’s editorial board now includes 34 scholars, which was expanded from 18 members in the First Edition. Representing the work of researchers from over 30 countries, the Encyclopedia is broad in scope, covering everything from authentication and identification to quantum cryptography and web security. The text’s practical style is instructional, yet fosters investigation. Each area presents concepts, designs, and specific implementations. The highly-structured essays in this work include synonyms, a definition and discussion of the topic, bibliographies, and links to related literature. Extensive cross-references to other entries within the Encyclopedia support efficient, user-friendly searches for immediate access to relevant information. Key concepts presented in the Encyclopedia of Cryptography and Security include: Authentication and identification; Block ciphers and stream ciphers; Computational issues; Copy protection; Cryptanalysis and security; Cryptographic protocols; Electronic payment and digital certificates; Elliptic curve cryptography; Factorization algorithms and primality tests; Hash functions and MACs; Historical systems; Identity-based cryptography; Implementation aspects for smart cards and standards; Key management; Multiparty computations like voting schemes; Public key cryptography; Quantum cryptography; Secret sharing schemes; Sequences; Web Security. Topics covered: Data Structures, Cryptography and Information Theory; Data Encryption; Coding and Information Theory; Appl.Mathematics/Computational Methods of Engineering; Applications of Mathematics; Complexity. This authoritative reference will be published in two formats: print and online. The online edition features hyperlinks to cross-references, in addition to significant research. |
| calibrating noise to sensitivity in private data analysis: Web Information Systems and Applications Guojun Wang, Xuemin Lin, James Hendler, Wei Song, Zhuoming Xu, Genggeng Liu, 2020-09-22 This book constitutes the proceedings of the 17th International Conference on Web Information Systems and Applications, WISA 2020, held in Guangzhou, China, in September 2020. The 42 full papers and 16 short papers presented were carefully reviewed and selected from 165 submissions. The papers are grouped in topical sections on world wide web, recommendation, query processing and algorithm, natural language processing, machine learning, graph query, edge computing and data mining, data privacy and security, and blockchain. |
| calibrating noise to sensitivity in private data analysis: Algorithms – ESA 2013 Hans L. Bodlaender, Giuseppe F. Italiano, 2013-08-16 This book constitutes the refereed proceedings of the 21st Annual European Symposium on Algorithms, ESA 2013, held in Sophia Antipolis, France, in September 2013 in the context of the combined conference ALGO 2013. The 69 revised full papers presented were carefully reviewed and selected from 303 initial submissions: 53 out of 229 in track Design and Analysis and 16 out of 74 in track Engineering and Applications. The papers in this book present original research in all areas of algorithmic research, including but not limited to: algorithm engineering; algorithmic aspects of networks; algorithmic game theory; approximation algorithms; computational biology; computational finance; computational geometry; combinatorial optimization; data compression; data structures; databases and information retrieval; distributed and parallel computing; graph algorithms; hierarchical memories; heuristics and meta-heuristics; mathematical programming; mobile computing; on-line algorithms; parameterized complexity; pattern matching; quantum computing; randomized algorithms; scheduling and resource allocation problems; streaming algorithms. |
| calibrating noise to sensitivity in private data analysis: Database Systems for Advanced Applications Yunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang, 2020-09-21 The 4 volume set LNCS 12112-12114 constitutes the papers of the 25th International Conference on Database Systems for Advanced Applications which will be held online in September 2020. The 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions. The conference program presents the state-of-the-art R&D activities in database systems and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry. |
| calibrating noise to sensitivity in private data analysis: Advances in Cryptology – EUROCRYPT 2019 Yuval Ishai, Vincent Rijmen, 2019-05-14 The three volume-set LNCS 11476, 11477, and 11478 constitute the thoroughly refereed proceedings of the 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, EUROCRYPT 2019,held in Darmstadt, Germany, in May 2019. The 76 full papers presented were carefully reviewed and selected from 327 submissions. The papers are organized into the following topical sections: ABE and CCA security; succinct arguments and secure messaging; obfuscation; block ciphers; differential privacy; bounds for symmetric cryptography; non-malleability; blockchain and consensus; homomorphic primitives; standards; searchable encryption and ORAM; proofs of work and space; secure computation; quantum, secure computation and NIZK, lattice-based cryptography; foundations; efficient secure computation; signatures; information-theoretic cryptography; and cryptanalysis. |
| calibrating noise to sensitivity in private data analysis: Cyber Security M. U. Bokhari, Namrata Agrawal, Dharmendra Saini, 2018-04-27 This book comprises select proceedings of the annual convention of the Computer Society of India. Divided into 10 topical volumes, the proceedings present papers on state-of-the-art research, surveys, and succinct reviews. The volume covers diverse topics ranging from information security to cryptography and from encryption to intrusion detection. This book focuses on Cyber Security. It aims at informing the readers about the technology in general and the internet in particular. The book uncovers the various nuances of information security, cyber security and its various dimensions. This book also covers latest security trends, ways to combat cyber threats including the detection and mitigation of security threats and risks. The contents of this book will prove useful to professionals and researchers alike. |
| calibrating noise to sensitivity in private data analysis: Systems, Software and Services Process Improvement Murat Yilmaz, Paul Clarke, Richard Messnarz, Michael Reiner, 2021-08-26 This volume constitutes the refereed proceedings of the 28th European Conference on Systems, Software and Services Process Improvement, EuroSPI 2021, held in Krems, Austria, in September 2021*. The 42 full papers and 9 short papers presented were carefully reviewed and selected from 100 submissions. The volume presents core research contributions and selected industrial contributions. Core research contributions: SPI and emerging software and systems engineering paradigms; SPI and team skills and diversity; SPI and recent innovations; SPI and agile; SPI and standards and safety and security norms; SPI and good/bad SPI practices in improvement; SPI and functional safety and cybersecurity; digitalisation of industry, infrastructure and e-mobility. Selected industrial contributions: SPI and emerging software and systems engineering paradigms; SPI and recent innovations; SPI and agile; SPI and standards and safety and security norms; SPI and good/bad SPI practices in improvement; SPI and functional safety and cybersecurity; digitalisation of industry, infrastructure and e-mobility; virtual reality. *The conference was partially held virtually due to the COVID-19 pandemic. |
| calibrating noise to sensitivity in private data analysis: Privacy-Enhancing Fog Computing and Its Applications Xiaodong Lin, Jianbing Ni, Xuemin (Sherman) Shen, 2018-11-12 This SpringerBrief covers the security and privacy challenges in fog computing, and proposes a new secure and privacy-preserving mechanisms to resolve these challenges for securing fog-assisted IoT applications. Chapter 1 introduces the architecture of fog-assisted IoT applications and the security and privacy challenges in fog computing. Chapter 2 reviews several promising privacy-enhancing techniques and illustrates examples on how to leverage these techniques to enhance the privacy of users in fog computing. Specifically, the authors divide the existing privacy-enhancing techniques into three categories: identity-hidden techniques, location privacy protection and data privacy enhancing techniques. The research is of great importance since security and privacy problems faced by fog computing impede the healthy development of its enabled IoT applications. With the advanced privacy-enhancing techniques, the authors propose three secure and privacy-preserving protocols for fog computing applications, including smart parking navigation, mobile crowdsensing and smart grid. Chapter 3 introduces identity privacy leakage in smart parking navigation systems, and proposes a privacy-preserving smart parking navigation system to prevent identity privacy exposure and support efficient parking guidance retrieval through road-side units (fogs) with high retrieving probability and security guarantees. Chapter 4 presents the location privacy leakage, during task allocation in mobile crowdsensing, and propose a strong privacy-preserving task allocation scheme that enables location-based task allocation and reputation-based report selection without exposing knowledge about the location and reputation for participators in mobile crowdsensing. Chapter 5 introduces the data privacy leakage in smart grid, and proposes an efficient and privacy-preserving smart metering protocol to allow collectors (fogs) to achieve real-time measurement collection with privacy-enhanced data aggregation. Finally, conclusions and future research directions are given in Chapter 6. This brief validates the significant feature extension and efficiency improvement of IoT devices without sacrificing the security and privacy of users against dishonest fog nodes. It also provides valuable insights on the security and privacy protection for fog-enabled IoT applications. Researchers and professionals who carry out research on security and privacy in wireless communication will want to purchase this SpringerBrief. Also, advanced level students, whose main research area is mobile network security will also be interested in this SpringerBrief. |
| calibrating noise to sensitivity in private data analysis: Data Science Beiji Zou, Min Li, Hongzhi Wang, Xianhua Song, Wei Xie, Zeguang Lu, 2017-09-15 This two volume set (CCIS 727 and 728) constitutes the refereed proceedings of the Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017 (originally ICYCSEE) held in Changsha, China, in September 2017. The 112 revised full papers presented in these two volumes were carefully reviewed and selected from 987 submissions. The papers cover a wide range of topics related to Basic Theory and Techniques for Data Science including Mathematical Issues in Data Science, Computational Theory for Data Science, Big Data Management and Applications, Data Quality and Data Preparation, Evaluation and Measurement in Data Science, Data Visualization, Big Data Mining and Knowledge Management, Infrastructure for Data Science, Machine Learning for Data Science, Data Security and Privacy, Applications of Data Science, Case Study of Data Science, Multimedia Data Management and Analysis, Data-driven Scientific Research, Data-driven Bioinformatics, Data-driven Healthcare, Data-driven Management, Data-driven eGovernment, Data-driven Smart City/Planet, Data Marketing and Economics, Social Media and Recommendation Systems, Data-driven Security, Data-driven Business Model Innovation, Social and/or organizational impacts of Data Science. |
Calibration - Wikipedia
In measurement technology and metrology, calibration is the comparison of measurement values delivered by a device under test with those of a calibration standard of known accuracy.
CALIBRATE Definition & Meaning - Merriam-Webster
The meaning of CALIBRATE is to ascertain the caliber of (something). How to use calibrate in a sentence.
CALIBRATING | English meaning - Cambridge Dictionary
CALIBRATING definition: 1. present participle of calibrate 2. to mark units of measurement on an instrument such so that it…. Learn more.
What Is Calibration? Understanding the Basics | Fluke
Calibration is the act of comparing a device under test (DUT) of an unknown value with a reference standard of a known value. A person typically performs a calibration to determine the error or …
Calibrating - definition of calibrating by The Free Dictionary
To check, adjust, or determine by comparison with a standard (the graduations of a quantitative measuring instrument): calibrate a thermometer. 2. a. To make corrections in; adjust: calibrated …
What Is Instrument Calibration and Why Is it Needed?
Apr 10, 2023 · Calibration is a process of comparing the output of measuring instruments and devices against the input signal of a standard, verified instrument. It detects and identifies any …
Calibration – Definition, types, purpose, Procedure of Calibration
The process of comparison of a device with unknown accuracy to a device with a known, accurate standard to eliminate any variation in the device being checked is called calibration.
Understanding the Calibration Method: A Comprehensive Guide
In this article, we will delve into the world of calibration, exploring its definition, importance, types, and applications. We will also discuss the steps involved in the calibration process and the …
Equipment Calibration Process Explained - Qse academy
In simple terms, equipment calibration is the process of checking and adjusting a measuring instrument to ensure it provides accurate readings. It involves comparing the instrument’s …
What is calibration? Calibration meaning and definition - Beamex
Calibration is key to ensuring accurate measurements and helping to improve efficiency, compliance, and safety, while minimizing emissions, waste, and risk.
Calibration - Wikipedia
In measurement technology and metrology, calibration is the comparison of measurement values delivered by a device under test with those of a calibration standard of known accuracy.
CALIBRATE Definition & Meaning - Merriam-Webster
The meaning of CALIBRATE is to ascertain the caliber of (something). How to use calibrate in a sentence.
CALIBRATING | English meaning - Cambridge Dictionary
CALIBRATING definition: 1. present participle of calibrate 2. to mark units of measurement on an instrument such so that it…. Learn more.
What Is Calibration? Understanding the Basics | Fluke
Calibration is the act of comparing a device under test (DUT) of an unknown value with a reference standard of a known value. A person typically performs a calibration to determine …
Calibrating - definition of calibrating by The Free Dictionary
To check, adjust, or determine by comparison with a standard (the graduations of a quantitative measuring instrument): calibrate a thermometer. 2. a. To make corrections in; adjust: …
What Is Instrument Calibration and Why Is it Needed?
Apr 10, 2023 · Calibration is a process of comparing the output of measuring instruments and devices against the input signal of a standard, verified instrument. It detects and identifies any …
Calibration – Definition, types, purpose, Procedure of Calibration
The process of comparison of a device with unknown accuracy to a device with a known, accurate standard to eliminate any variation in the device being checked is called calibration.
Understanding the Calibration Method: A Comprehensive Guide
In this article, we will delve into the world of calibration, exploring its definition, importance, types, and applications. We will also discuss the steps involved in the calibration process and the …
Equipment Calibration Process Explained - Qse academy
In simple terms, equipment calibration is the process of checking and adjusting a measuring instrument to ensure it provides accurate readings. It involves comparing the instrument’s …
What is calibration? Calibration meaning and definition - Beamex
Calibration is key to ensuring accurate measurements and helping to improve efficiency, compliance, and safety, while minimizing emissions, waste, and risk.
