CCS'15: The 22nd ACM Conference on Computer and Communications Security Oct 12, 2015-Oct 16, 2015 Denver, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security Oct 30, 2017-Nov 03, 2017 Dallas, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security Oct 24, 2016-Oct 28, 2016 Vienna, Austria. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

We are pleased to present herein the proceedings of the 13th ACM Symposium on Information, Computer and Communications Security (ASIACCS 2018) held in Incheon, Korea, June 4-8, 2018. ASIACCS 2018 is organized by AsiaCCS 2018 organizing committee, supported by ACM SigSAC, Korea Institute of Information Security &Cryptography (KIISC). We received 310 submissions. This year's Program Committee comprising 103 security researchers from 24 countries, helped by 73 external reviewers, evaluated these submissions through thoughtful discussion and rigorous review procedure. The review process resulted in 52 full papers being accepted to the program, representing an acceptance rate of about 17%. In addition, 10 short papers and 15 posters are also included in the program. Once again we have a very strong technical program along with 5 specialized pre-conference workshops: CPSS'18, APKC'18, RESEC'18, BBC'18, and SCC'18. We are also fortunate to have distinguished invited speakers, Dr. Cliff Wang, Dr. Jaeyeon Jung, and Prof. Kevin Fu, who will provide various insights into Cyber Deception: an emergent research area, Securing a large scale IoT ecosystem, and Analog Sensor Cybersecurity and Transduction Attacks, respectively. These valuable and insightful talks can and will guide us to a better understanding on both fundamental and emerging topic areas in the field of information, computer and communications security.

This book of Advances in Intelligent and Soft Computing contains accepted papers presented at CISIS 2021 and ICEUTE 2021, all conferences held in the beautiful and historic city of Bilbao (Spain), in September 2021. The aim of the 14th CISIS 20121 conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of computational intelligence, information security, and data mining. The need for intelligent, flexible behavior by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event. After a through peer-review process, the CISIS 2021 International Program Committee selected 23 papers which are published in these conference proceedings achieving an acceptance rate of 40%. In this relevant edition, a special emphasis was put on the organization of special sessions. One special session is organized related to relevant topics as follows: building trust in ecosystems and ecosystem components. In the case of 12th ICEUTE 2021, the International Program Committee selected 17 papers, which are published in these conference proceedings. One special session is organized related to relevant topics as follows: sustainable personal goals: engaging students in their learning process. The selection of papers is extremely rigorous in order to maintain the high quality of the conference, and we would like to thank the members of the program committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference, and the CISIS and ICEUTE conferences would not exist without their help.

This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”