Advances In Artificial Intelligence For Privacy Protection And Security.

In this book, we aim to collect the most recent advances in artificial intelligence techniques (i.e. neural networks, fuzzy systems, multi-agent systems, genetic algorithms, image analysis, clustering, etc), which are applied to the protection of privacy and security. The symbiosis between these fie...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico eBook
Idioma:Inglés
Publicado: World Scientific 2009.
Temas:
Information technology > Security measures.
Computer security.
Data protection.
Artificial intelligence.
Technologie de l'information > Sécurité > Mesures.
Sécurité informatique.
Protection de l'information (Informatique)
Intelligence artificielle.
artificial intelligence.
Artificial intelligence
Computer security
Data protection
Information technology > Security measures
Electronic resource.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover13;
  • Contents
  • Preface
  • 1. Introduction A. Solanas and A. Mart182;179;nez-Ballest182;e
  • 1.1. Organization of the book
  • References
  • PART 1: A Brief Introduction to Privacy and Security
  • 2. An Introduction to Privacy Aspects of Information and Communication Technologies A. Martinez-Balleste and A. Solanas
  • Contents
  • 2.1. Introduction
  • 2.2. Privacy and the Internet
  • 2.3. Privacy in Databases
  • 2.4. Privacy in Ubiquitous Computing
  • 2.5. Conclusions
  • Disclaimer and Acknowledgements
  • References
  • 3. An Overview of Information Security A. Ribagorda Garnacho, A.I. Gonz182;alez-Tablas Ferreres, A. Alcaide Raya
  • Contents
  • 3.1. Introduction
  • 3.2. Vulnerabilities
  • 3.3. Threats
  • 3.4. Countermeasures
  • 3.5. Authentication mechanisms
  • 3.6. Access control mechanisms
  • 3.7. Data encipherment mechanisms
  • 3.8. Digital signature mechanism
  • 3.9. Digital certificates
  • 3.10. Audit logs
  • 3.11. Physical security
  • References
  • PART 2: Privacy Protection by means of Arti175;cial Intelligence
  • 4. Data Mining in Large Databases ' Strategies for Managing the Trade-O174; Between Societal Bene175;t and Individual Privacy M. Schmid
  • Contents
  • 4.1. Introduction
  • 4.2. Examples of data-collecting institutions and data users
  • 4.3. Strategies for controlling privacy
  • 4.4. Measures of the utility of published data sets and outputs
  • 4.5. Conclusion
  • References
  • 5. Desemantization for Numerical Microdata Anonymization J. Pont-Tuset, J. Nin, P. Medrano-Gracia, J.-Ll. Larriba-Pey and V. Munt182;es-Mulero
  • Contents
  • 5.1. Introduction
  • 5.2. Background and State of the Art
  • 5.3. A New Desemantization Methodology
  • 5.4. Data Preprocessing
  • 5.5. Data Fitting
  • 5.6. Experiments
  • 5.7. Conclusions
  • Acknowledgments
  • References
  • 6. Multi-Objective Evolutionary Optimization in Statistical Disclosure Control R. Dewri, I. Ray, I. Ray and D. Whitley
  • Contents
  • 6.1. Introduction
  • 6.2. Multi-objective Optimization
  • 6.3. Statistical Disclosure Control
  • 6.4. Evolutionary Optimization
  • 6.5. Some Empirical Results
  • 6.6. Summary
  • Acknowledgment
  • References
  • 7. On the Definition of Cluster-Speci175;c Information Loss Measures V. Torra
  • Contents
  • 7.1. Introduction
  • 7.2. Preliminaries
  • 7.3. Information loss measures for clustering
  • 7.4. Conclusions and future work
  • Acknowledgments
  • References
  • 8. Privacy Preserving and Use of Medical Information in a Multiagent System K. Gibert, A. Valls, L. Lhotska and P. Aubrecht
  • Contents
  • 8.1. Introduction
  • 8.2. Privacy preserving and security in a distributed platform for medical domains
  • 8.3. Identification and authentication
  • 8.4. Authorization and information access rights
  • 8.5. Multiagent system
  • 8.6. Intermediate layer for knowledge-interface communications
  • 8.7. Private data protection
  • 8.8. A real case: the K4Care project
  • 8.9. Discussion
  • References
  • PART 3: Security by means of Artificial Intelligence
  • 9. Perimeter Security on Noise-Robust Vehicle Detection Using Nonlinear Hebbian Learning B. Lu, A. Dibazar, S. George and T.W. Berger
  • Contents
  • 9.1. Introduction
  • 9.2. Description of the Proposed System
  • 9.3. Unsupervised Nonlinear Hebbian Learning
  • 9.4. Real-time Field T.

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