This book constitutes the proceedings of the 24th Annual IFIP WG 11.3 Working Conference on Data and Applications Security, held in Rome Italy in June 2010. The 18 full and 11 short papers presented in this volume were carefully reviewed and selected from 61 submissions. The topics covered are query and data privacy; data protection; access control; data confidentiality and query verification; policy definition and enforcement; and trust and identity management.
This book constitutes the refereed proceedings of the 34th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2020, held in Regensburg, Germany, in June 2020.* The 14 full papers and 8 short papers presented were carefully reviewed and selected from 39 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections named network and cyber-physical systems security; information flow and access control; privacy-preserving computation; visualization and analytics for security; spatial systems and crowdsourcing security; and secure outsourcing and privacy. *The conference was held virtually due to the COVID-19 pandemic.
This book constitutes the refereed proceedings of the 28th IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2014, held in Vienna, Austria, in July 2014. The 22 revised full papers and 4 short papers presented were carefully reviewed and selected from 63 submissions. The papers are organized in topical sections on access control, privacy, networked and mobile environments, data access, cloud databases, and private retrieval.
This book constitutes the refereed proceedings of the 29th Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2015, held in Fairfax, VA, USA, in July 2015. The 18 revised full papers and 6 short papers presented were carefully reviewed and selected from 45 submissions. The papers are organized in the following topical sections: data anonymization and computation; access control and authorization; user privacy; authentication and information integration; privacy and trust; access control and usage policies; network and internet security; and information flow and inference.
This book constitutes the refereed proceedings of the 27th IFIP WG 11.3 International Conference on Data and Applications Security and Privacy, DBSec 2013, held in Newark, NJ, USA in July 2013. The 16 revised full and 6 short papers presented were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on privacy, access control, cloud computing, data outsourcing, and mobile computing.
Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.
This book constitutes the refereed proceedings of the 5th International Symposium on Engineering Secure Software and Systems, ESSoS 2013, held in Paris, France, in February/March 2013. The 13 revised full papers presented together with two idea papers were carefully reviewed and selected from 62 submissions. The papers are organized in topical sections on secure programming, policies, proving, formal methods, and analyzing.
With an ever-increasing amount of information on the web, it is critical to understand the pedigree, quality, and accuracy of your data. Using provenance, you can ascertain the quality of data based on its ancestral data and derivations, track back to sources of errors, allow automatic re-enactment of derivations to update data, and provide attribution of the data source. Secure Data Provenance and Inference Control with Semantic Web supplies step-by-step instructions on how to secure the provenance of your data to make sure it is safe from inference attacks. It details the design and implementation of a policy engine for provenance of data and presents case studies that illustrate solutions in a typical distributed health care system for hospitals. Although the case studies describe solutions in the health care domain, you can easily apply the methods presented in the book to a range of other domains. The book describes the design and implementation of a policy engine for provenance and demonstrates the use of Semantic Web technologies and cloud computing technologies to enhance the scalability of solutions. It covers Semantic Web technologies for the representation and reasoning of the provenance of the data and provides a unifying framework for securing provenance that can help to address the various criteria of your information systems. Illustrating key concepts and practical techniques, the book considers cloud computing technologies that can enhance the scalability of solutions. After reading this book you will be better prepared to keep up with the on-going development of the prototypes, products, tools, and standards for secure data management, secure Semantic Web, secure web services, and secure cloud computing.
This book constitutes the refereed proceedings of the 8th International Conference on Trust and Privacy in Digital Business, TrustBus 2011, held in Toulouse, France, in August/September 2011 in conjunction with DEXA 2011. The 18 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in the following topical sections: identity and trust management; security and privacy models for pervasive information systems; reliability and security of content and data; authentication and authorization in digital business; intrusion detection and information filtering; management of privacy and confidentiality; and cryptographic protocols/usability of security.
Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.
