OSSDIP: Open Source Secure Data Infrastructure and Processes Platform that Supports Data Visiting Martin Weise 1 Tobias Grantner 1 Lukas Mahler 1 Andreas Rauber 1 Problem Description In an increasing number of settings, both researchers in academia as well as stakeholders in industry need to safe- guard access to highly sensitive data. Analyst-VM (Compute) Desktop-VM (Access) Owner-VM (Ingest) Data Owner Analyst want share data want access data provide data work requires Figure 1: Social architecture (highlight) While data sharing is being proclaimed as the future in open science, many settings do not allow for such ap- proaches due to e.g. confidentiality concerns. Methodology Based on the UK HDRA Trusted Research Environments (TRE) definition and experience of operating DEXHELPP for almost ten years. Provide highly controlled and moni- tored data visiting services, without disseminating an ac- tual copy: • Components of data anonymization and fingerprinting • Extensive logging and monitoring • Defined processes and contractual frameworks Secure Data Infrastructure The overall concept is centered around the principle of never providing access to the data node where all data is being held. For each individual analysis request: • Specific subset of the data required is extracted from the data node, and • Copied onto a dedicated Analyst-VM, together with the tools required to perform the analysis Proposal RQs, Tools, etc. Desktop-VM (Access) Proposal RQs, Tools, etc. Monitoring Logs, etc. Analyst-VM (Compute) Subset RStudio (Data Science) KNIME (Analytics) Data Node (Sensitive Data) Jupyter (Data Science) Figure 2: Data access workflow (highlight) Access to this Analyst-VM is granted to the analyst working on the task at hand – however, never directly, but only via a dedicated Remote Desktop-VM to introduce a media break and avoid any data flowing off via e.g. a tunnel. Research Activities via Data Visiting A dedicated Remote Desktop-VM is created to provide the sole access to the Analyst-VM. The Analyst then can ana- lyze the data as long as the time-out is not reached. Figure 3: Working with the raw data in Jupyter, RStudio and KNIME Our reference implementation supports open-source tools by default: • Data science: Jupyter Notebooks, RStudio, KNIME • Text processing: Libreoffice, LATEX • Programming: Python, Java, R It can be extended to support commercial software as well in the configuration files Conclusions The current state of the secure data infrastructure allows a Data Owner to invite experts (e.g. Analyst) to visit sensi- tive data on a trusted meeting point (Analyst-VM). OSS- DIP requires at least three (optimally nine) physical ma- chines on trusted hardware. Contact Technical lead Projektass. Dipl.-Ing. Martin Weise looks forward for your e-mail: martin.weise@tuwien.ac.at References [1] Project Website. https://ossdip.at/. [2] M. Weise, F. Kovacevic, N. Popper, and A. Rauber. OSS- DIP: Open Source Secure Data Infrastructure and Pro- cesses Supporting Data Visiting. Data Science Journal, 21:4, 2022. doi:10.5334/dsj-2022-004. [3] M. Weise and A. Rauber. A Data-Visiting Infrastruc- ture for Providing Access to Preserved Databases that Cannot be Shared or Made Publicly Accessible. In Pro- ceedings of the 17th International Conference on Digital Preservation, 2021. doi:10.17605/OSF.IO/VKN4R. 1Vienna University of Technology