[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

Trust: The Social Virtues and the Creation of Prosperity

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Data classification has become an important and prevailing technique for big data analytics. Typically, a data classifier is designed and outsourced to a public cloud. A service provider then can easily provide various services and handle frequent and massive classification requests from users. With privacy concerns as well as Intellectual Property(IP) protection issues, the valuable classifier and the sensitive user data cannot be directly exposed to the public cloud. In this paper, we focus on the Support Vector Machine (SVM), one of the most popular classifiers, and propose an efficient and privacy-preserving outsourcing scheme for SVM classification in public clouds. Specifically, the service provider is allowed to transform the traditional SVM classifier to fixed hyper-rectangles and the order-preserving encryption is utilized to encrypt these hyper-rectangles as the encrypted classifier. Afterwards, the encrypted classifier is outsourced to the public cloud, and a user can submit an encrypted range query to the cloud and obtain the classification results back. Security analysis and extensive experimental evaluation demonstrate that our scheme can protect the confidentiality of classifier and users' data and achieves efficient SVM classification in terms of computational cost.

Efficient and Privacy-Preserving Outsourced SVM Classification in Public Cloud

Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications. The book is divided into four sections: introduction to Big Data, networking theory and design for Big Data, networking security for Big Data, and platforms and systems for Big Data applications. Focusing on key networking issues in Big Data, the book explains network design and implementation for Big Data. It examines how network topology impacts data collection and explores Big Data storage and resource management. Addresses the virtual machine placement problem Describes widespread network and information security technologies for Big Data Explores network configuration and flow scheduling for Big Data applications Presents a systematic set of techniques that optimize throughput and improve bandwidth for efficient Big Data transfer on the Internet Tackles the trade-off problem between energy efficiency and service resiliency The book covers distributed Big Data storage and retrieval as well as security, trust, and privacy protection for Big Data collection, storage, and search. It discusses the use of cloud infrastructures and highlights its benefits to overcome the identified issues and to provide new approaches for managing huge volumes of heterogeneous data. The text concludes by proposing an innovative user data profile-aware policy-based network management framework that can help you exploit and differentiate user data profiles to achieve better power efficiency and optimized resource management.

Networking for Big Data

Along with gaining popularity of Real-Time Bidding (RTB) based programmatic advertising, the click farm based invalid traffic, which leverages massive real smartphones to carry out large-scale ad fraud campaigns, is becoming one of the major threats against online advertisement. In this study, we take an initial step towards the detection and large-scale measurement of the click farm based invalid traffic. Our study begins with a measurement on the device's features using a real-world labeled dataset, which reveals a series of features distinguishing the fraudulent devices from the benign ones. Based on these features, we develop EvilHunter, a system for detecting fraudulent devices through ad bid request logs with a focus on clustering fraudulent devices. EvilHunter functions by 1) building a classifier to distinguish fraudulent and benign devices; 2) clustering devices based on app usage patterns; and 3) relabeling devices in clusters through majority voting. EvilHunter demonstrates 97% precision and 95% recall on a real-world labeled dataset. By investigating a super click farm, we reveal several cheating strategies that are commonly adopted by fraudulent clusters. We further reduce the overhead of EvilHunter and discuss how to deploy the optimized EvilHunter in a real-world system. We are in partnership with a leading ad verification company to integrate EvilHunter into their industrial platform.

Understanding and Detecting Mobile Ad Fraud Through the Lens of Invalid Traffic

A clique-based secure admission control scheme for mobile ad hoc networks (MANETs)

Data provenance, which records the history of the ownership of a document, as well as the actions performed on it, has received great attention in recent years. However, the privacy issue in data provenance has not been well investigated. In this paper, to simultaneously protect the security of data provenance and achieve user privacy preservation, we propose a new privacy-preserving data provenance (PDP) scheme. With the proposed PDP scheme, users can anonymously access the remoter servers and execute the secure provenance operations. Detailed security analysis demonstrates the security of the proposed PDP scheme, In addition, extensive efficiency analyses have also been conducted to examine its superior efficiency in terms of secure provenance storage and verification costs.

Xiaodong Lin

Papers

Efficient and Privacy-Preserving Outsourced SVM Classification in Public Cloud

Networking for Big Data

Understanding and Detecting Mobile Ad Fraud Through the Lens of Invalid Traffic

A clique-based secure admission control scheme for mobile ad hoc networks (MANETs)

PDP: A Privacy-Preserving Data Provenance Scheme