Cover image for Behavior dynamics in media-sharing social networks
Title:
Behavior dynamics in media-sharing social networks
Personal Author:
Publication Information:
Cambridge ; New York : Cambridge University Press, 2011
Physical Description:
xii, 337 p. : ill. ; 26 cm.
ISBN:
9780521197274

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30000010322068 HM742 Z43 2011 Open Access Book Book
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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

Prefacep. xi
Part I Introductionp. 1
1 Introduction to media-sharing social networksp. 3
1.1 Quantitative analysis of social networksp. 5
1.2 Understanding media semantics in media-sharing networksp. 10
2 Overview of multimedia fingerprintingp. 14
2.1 Traitor-tracing multimedia fingerprintingp. 15
2.2 Scalable video coding systemp. 17
2.3 Scalable video fingerprintingp. 18
3 Overview of mesh-pull peer-to-peer video streamingp. 24
3.1 Mesh-pull structure for P2P video streamingp. 25
3.2 User dynamics in peer-to-peer video streamingp. 33
4 Game theory for social networksp. 41
4.1 Noncooperative and cooperative gamesp. 42
4.2 Noncooperative gamesp. 43
4.3 Bargaining gamesp. 50
Part II Behavior forensics in media-sharing social networksp. 55
5 Equal-risk fairness in colluder social networksp. 57
5.1 Equal-risk collusionp. 57
5.2 Influence on the detector's side: collusion resistancep. 63
5.3 Traitor-tracing capability of scalable fingerprintsp. 75
5.4 Chapter summary and bibliographical notesp. 82
6 Leveraging side information in colluder social networksp. 85
6.1 Probing and using side informationp. 85
6.2 Game-theoretic analysis of colluder detector dynamicsp. 93
6.3 Equilibrium analysisp. 94
6.4 Simulation resultsp. 103
6.5 Chapter summary and bibliographical notesp. 109
7 Risk-distortion analysis of multiuser collusionp. 111
7.1 Video fingerprintingp. 112
7.2 Risk-distortion modelingp. 113
7.3 Strategies with side informationp. 117
7.4 Parameter estimationp. 122
7.5 Simulation resultsp. 122
7.6 Chapter summary and bibliographical notesp. 127
Part III Fairness and cooperation stimulationp. 129
8 Game-theoretic modeling of colluder social networksp. 131
8.1 Multiuser collusion gamep. 132
8.2 Feasible and Pareto optimal collusionp. 137
8.3 When to colludep. 139
8.4 How to collude: the bargaining modelp. 150
8.5 How to collude: examplesp. 155
8.6 Maximum payoff collusionp. 160
8.7 Chapter summary and bibliographical notesp. 167
9 Cooperation stimulation in peer-to-peer video streamingp. 169
9.1 Incentives for peer cooperation over the Internetp. 170
9.2 Wireless peer-to-peer video streamingp. 178
9.3 Optimal cooperation strategies for wireless video streamingp. 181
9.4 Optimal chunk request algorithm for P2P video streaming with scalable codingp. 189
9.5 Chapter summary and bibliographical notesp. 193
10 Optimal pricing for mobile video streamingp. 195
10.1 Introductionp. 195
10.2 System modelp. 196
10.3 Optimal strategies for single secondary buyerp. 198
10.4 Multiple secondary buyersp. 206
10.5 Optimal pricing for the content ownerp. 208
10.6 Chapter summary and bibliographical notesp. 217
Part IV Misbehaving user identificationp. 219
11 Cheating behavior in colluder social networksp. 221
11.1 Traitors within traitors via temporal filteringp. 222
11.2 Traitors within traitors in scalable fingerprinting systemsp. 227
11.3 Chapter summaryp. 245
12 Attack resistance in peer-to-peer video streamingp. 247
12.1 Attack-resistant cooperation strategies in P2P video streaming over the Internetp. 248
12.2 Attack-resistant cooperation strategies in wireless P2P video streamingp. 261
12.3 Chapter summary and bibliographical notesp. 273
Part V Media-sharing social network structuresp. 275
13 Misbehavior detection in colluder social networks with different structuresp. 277
13.1 Behavior dynamics in colluder social networksp. 278
13.2 Centralized colluder social networks with trusted ringleadersp. 280
13.3 Distributed peer-structured colluder social networksp. 289
13.4 Chapter summary and bibliographical notesp. 306
14 Structuring cooperation for hybrid peer-to-peer streamingp. 308
14.1 System model and utility functionp. 309
14.2 Agent selection within a homogeneous groupp. 311
14.3 Agent selection within a heterogeneous groupp. 317
14.4 Distributed learning algorithm for ESSp. 320
14.5 Simulation resultsp. 320
14.6 Chapter summary and bibliographical notesp. 325
Referencesp. 326
Indexp. 335