Service Oriented Computing (SoC) has become the cornerstone of modern applications, shaping how we create and consume these software services. Consequently, we entrust these services with the most private and confidential data, making security, accountability and privacy-preserving service design paramount.
This workshop aims to foster in-depth discussion on cutting-edge approaches in addressing security, privacy and accountability concerns in data-driven systems. Furthermore, we seek topics related to security governance, where security policies, compliance and requirements will be considered in SoC. Additionally, new approaches for securing SoC are welcome and encouraged, e.g., the application of approaches such as AI and blockchain.
Similarly, for privacy, we also welcome case studies and research papers on topics such as privacy-by-design, and accountability in data sharing systems as valuable additions. Moreover, we consider software-driven trust and accountability as foundational building blocks for secure and privacy-preserving interactions in SoC. Thus, we welcome contributions highlighting the integration of trustless accountable systems in data-driven architectures.
Lastly, we welcome topics considering efficiencies in data-driven while maintaining security, privacy and accountability in service-oriented architectures, e.g., carbon-aware data spaces, serverless data-driven and data-sharing systems
We invite researchers and practitioners to submit original contributions related to, but not limited to, the following topics:
We welcome original research papers, case studies, and position papers. Papers should be formatted according to Springer’s LNCS Formatting Guidelines, not exceeding 12 pages.
Submit HERE your paper!
Each paper must be submitted on or before the provided deadlines. The final submission should be formatted according to Springer’s LNCS Camera-ready instructions. Each paper will be reviewed by at least three reviewers.
Technical University of Berlin
Politecnico di Milano
Introduction of SAPD and ASOCA
XPS++: A Publish/Subscribe System with Built-in Security and Privacy by Design
Federated Data Products: A Confluence of Data Mesh and Gaia-X for Data Sharing
Non-Expert Level Analysis of Self-Adaptive Systems
SAPD and ASOCA OC
Jeffar, Farouk; Plebani, Pierluigi
Claudia Raibulet, Xiaojun Ling
SAPD and ASOCA OC