Abstract While approaches aimed at developing forensic-ready systems are starting to emerge, it is still primarily a theoretical concept. This paper presents a case study of integrating forensic readiness capabilities into SensitiveCloud, an information system for storing and processing sensitive data. A risk-based approach to forensic readiness design is followed to achieve it. Consequently, weaknesses in both processes and systems are identified, and forensic readiness requirements are formulated. This case study reports on lessons learned in a practical implementation of a forensic-ready system, its impact on security, and its support towards ISO/IEC 27k.

Digital mathematical libraries (DMLs) such as arXiv, Numdam, and EuDML contain mainly documents from STEM fields, where mathematical formulae are often more important than text for understanding. Conventional information retrieval (IR) systems are unable to represent formulae and they are therefore ill-suited for math information retrieval (MIR). To fill the gap, we have developed, and open-sourced the MIaS MIR system. MIaS is based on the full-text search engine Apache Lucene. On top of text retrieval, MIaS also incorporates a set of tools for preprocessing mathematical formulae. We describe the design of the system and present speed, and quality evaluation results. We show that MIaS is both efficient, and effective, as evidenced by our victory in the NTCIR-11 Math-2 task.