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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010322068 | HM742 Z43 2011 | Open Access Book | Book | Searching... |
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Summary
Summary
In large-scale media-sharing social networks, where millions of users create, share, link and reuse media content, there are clear challenges in protecting content security and intellectual property, and in designing scalable and reliable networks capable of handling high levels of traffic. This comprehensive resource demonstrates how game theory can be used to model user dynamics and optimize design of media-sharing networks. It reviews the fundamental methodologies used to model and analyze human behavior, using examples from real-world multimedia social networks. With a thorough investigation of the impact of human factors on multimedia system design, this accessible book shows how an understanding of human behavior can be used to improve system performance. Bringing together mathematical tools and engineering concepts with ideas from sociology and human behavior analysis, this one-stop guide will enable researchers to explore this emerging field further and ultimately design media-sharing systems with more efficient, secure and personalized services.
Author Notes
H.Vicky Zhao is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Alberta. The recipient of the IEEE Signal Processing Society - Young Author Best Paper Award 2008, she is an Associate Editor for the IEEE Signal Processing Letters and the Journal of Visual Communication and Image Representation.
W. Sabrina Lin is a Research Associate in the Department of Electrical and Computer Engineering at the University of Maryland. She received the University of Maryland Future Faculty Fellowship in 2007.
K. J. Ray Liu is a Distinguished Scholar-Teacher of the University of Maryland, where he is Christine Kim Eminent Professor in Information Technology. He received the IEEE Signal Processing Society Technical Achievement Award in 2009, and was Editor-in-Chief of the IEEE Signal Processing Magazine and the founding Editor-in-Chief of the EURASIP Journal on Advances in Signal Processing.
Table of Contents
Preface | p. xi |
Part I Introduction | p. 1 |
1 Introduction to media-sharing social networks | p. 3 |
1.1 Quantitative analysis of social networks | p. 5 |
1.2 Understanding media semantics in media-sharing networks | p. 10 |
2 Overview of multimedia fingerprinting | p. 14 |
2.1 Traitor-tracing multimedia fingerprinting | p. 15 |
2.2 Scalable video coding system | p. 17 |
2.3 Scalable video fingerprinting | p. 18 |
3 Overview of mesh-pull peer-to-peer video streaming | p. 24 |
3.1 Mesh-pull structure for P2P video streaming | p. 25 |
3.2 User dynamics in peer-to-peer video streaming | p. 33 |
4 Game theory for social networks | p. 41 |
4.1 Noncooperative and cooperative games | p. 42 |
4.2 Noncooperative games | p. 43 |
4.3 Bargaining games | p. 50 |
Part II Behavior forensics in media-sharing social networks | p. 55 |
5 Equal-risk fairness in colluder social networks | p. 57 |
5.1 Equal-risk collusion | p. 57 |
5.2 Influence on the detector's side: collusion resistance | p. 63 |
5.3 Traitor-tracing capability of scalable fingerprints | p. 75 |
5.4 Chapter summary and bibliographical notes | p. 82 |
6 Leveraging side information in colluder social networks | p. 85 |
6.1 Probing and using side information | p. 85 |
6.2 Game-theoretic analysis of colluder detector dynamics | p. 93 |
6.3 Equilibrium analysis | p. 94 |
6.4 Simulation results | p. 103 |
6.5 Chapter summary and bibliographical notes | p. 109 |
7 Risk-distortion analysis of multiuser collusion | p. 111 |
7.1 Video fingerprinting | p. 112 |
7.2 Risk-distortion modeling | p. 113 |
7.3 Strategies with side information | p. 117 |
7.4 Parameter estimation | p. 122 |
7.5 Simulation results | p. 122 |
7.6 Chapter summary and bibliographical notes | p. 127 |
Part III Fairness and cooperation stimulation | p. 129 |
8 Game-theoretic modeling of colluder social networks | p. 131 |
8.1 Multiuser collusion game | p. 132 |
8.2 Feasible and Pareto optimal collusion | p. 137 |
8.3 When to collude | p. 139 |
8.4 How to collude: the bargaining model | p. 150 |
8.5 How to collude: examples | p. 155 |
8.6 Maximum payoff collusion | p. 160 |
8.7 Chapter summary and bibliographical notes | p. 167 |
9 Cooperation stimulation in peer-to-peer video streaming | p. 169 |
9.1 Incentives for peer cooperation over the Internet | p. 170 |
9.2 Wireless peer-to-peer video streaming | p. 178 |
9.3 Optimal cooperation strategies for wireless video streaming | p. 181 |
9.4 Optimal chunk request algorithm for P2P video streaming with scalable coding | p. 189 |
9.5 Chapter summary and bibliographical notes | p. 193 |
10 Optimal pricing for mobile video streaming | p. 195 |
10.1 Introduction | p. 195 |
10.2 System model | p. 196 |
10.3 Optimal strategies for single secondary buyer | p. 198 |
10.4 Multiple secondary buyers | p. 206 |
10.5 Optimal pricing for the content owner | p. 208 |
10.6 Chapter summary and bibliographical notes | p. 217 |
Part IV Misbehaving user identification | p. 219 |
11 Cheating behavior in colluder social networks | p. 221 |
11.1 Traitors within traitors via temporal filtering | p. 222 |
11.2 Traitors within traitors in scalable fingerprinting systems | p. 227 |
11.3 Chapter summary | p. 245 |
12 Attack resistance in peer-to-peer video streaming | p. 247 |
12.1 Attack-resistant cooperation strategies in P2P video streaming over the Internet | p. 248 |
12.2 Attack-resistant cooperation strategies in wireless P2P video streaming | p. 261 |
12.3 Chapter summary and bibliographical notes | p. 273 |
Part V Media-sharing social network structures | p. 275 |
13 Misbehavior detection in colluder social networks with different structures | p. 277 |
13.1 Behavior dynamics in colluder social networks | p. 278 |
13.2 Centralized colluder social networks with trusted ringleaders | p. 280 |
13.3 Distributed peer-structured colluder social networks | p. 289 |
13.4 Chapter summary and bibliographical notes | p. 306 |
14 Structuring cooperation for hybrid peer-to-peer streaming | p. 308 |
14.1 System model and utility function | p. 309 |
14.2 Agent selection within a homogeneous group | p. 311 |
14.3 Agent selection within a heterogeneous group | p. 317 |
14.4 Distributed learning algorithm for ESS | p. 320 |
14.5 Simulation results | p. 320 |
14.6 Chapter summary and bibliographical notes | p. 325 |
References | p. 326 |
Index | p. 335 |