- When\nshould secure data and IT infrastructures be used?
\nWhenever the research data is confidential. <\/li> - How\ndo you know if data is confidential?
\nPlease read the guidelines in this post.<\/li><\/ul>\n\n\n\nConfidentiality checks<\/h2>\n\n\n\n
Research\ndata may be classified as confidential if:<\/p>\n\n\n\n
- It concerns sensitive personal data (FADP Art. 3c<\/a>), e.g. health-related or medical data.<\/li>
- It is either an identification (direct or indirect) or a pseudonymisation (i.e. a real name replaced by a code).<\/li>
- More generally, it is information specific to a limited, explicitly defined group of recipients (RSETHZ 203.25<\/a>).<\/li><\/ol>\n\n\n\n
Disclaimer of liability<\/h2>\n\n\n\n
The classification of research data as confidential is based on the SPHN IT Security Policy<\/a> and the Leonhard Med Acceptable Use Policy<\/a>. <\/p>\n\n\n\n
Leonhard Med: a secure scientific platform for\nconfidential data <\/h2>\n\n\n\n
A secure,\npowerful and versatile IT infrastructure for research on confidential data at\nETH Zurich. <\/p>\n\n\n\n
The provision\nof IT solutions for scientific research involving confidential data (e.g.\nmedical data) is a major challenge. It requires the fulfilment of three main\ncriteria. These cover a broad field of responsibilities and are at the same\ntime difficult to achieve: <\/p>\n\n\n\n
- Security\nand compliance <\/li>
- Flexibility and ease of use <\/li>
- Scalability\nand performance <\/li><\/ol>\n\n\n\n
Leonhard Med,\noperated by ETH Zurich Scientific IT Services (ITS SIS), is a new data and\ncomputing environment that harmonises these criteria. To achieve this, we have\ncombined established and innovative technical building blocks in novel ways and\nat the same time developed a suitable regulatory framework. Although it has\nbeen used in production since early 2018, it has had to be continuously\ndeveloped to keep pace with new and changing requirements in a rapidly changing\nscientific environment. While there are other comparable initiatives outside\nSwitzerland, each with different objectives and limitations, nothing similar\nhas been developed in Switzerland to date. <\/p>\n\n\n\n
Leonhard Med for data-driven precision medicine and\npersonalised health research <\/h2>\n\n\n\n
Leonhard Med\nenables collaborative, large-scale and very diverse biomedical research\n(including academies and hospitals) at ETH Zurich. In addition, Leonhard Med is\npart of an emerging national network of secure data centres (SPHN\/BioMedIT).\nThis puts ETH Zurich in a leading position to be the top-choice computer\ncentre, both for large-scale projects with confidential research data at\nnational and international levels and to teach students in such projects. <\/p>\n\n\n\n
First SPHN data protection and IT security training\nsession<\/h2>\n\n\n\n
Data-driven personalised medical treatment and prevention requires special IT and data infrastructures that combine several key concepts: security, privacy and user-friendliness for the individual on one hand, and promotion of research on the other. Leonhard Med is one of the first such infrastructures to be developed in Switzerland. The use of secure IT infrastructures when working with confidential data requires an awareness of data protection, the relevant laws and information security. For this reason, and in collaboration with colleagues from the Swiss Institute of Bioinformatics (SIB), we have developed a novel training course for researchers and IT personnel. This work was commissioned and supported by the Swiss Personalised Health Network (SPHN, see https:\/\/www.sphn.ch\/en.html<\/a>).<\/p>\n\n\n\n
On 20 November\n2018, the first SPHN data protection and IT security training session took\nplace at ETH Zurich: Heinz Stockinger (SIB) and Diana Coman Schmid (ITS SIS)\npresented guidelines on what needs to be done in practice to protect patient\nprivacy in biomedical research. Participants learned about their privacy\nresponsibilities, current regulations, anonymisation and confidentiality of\ndata, and IT security best practices to be aware of as a user.<\/p>\n\n\n\n
- It concerns sensitive personal data (FADP Art. 3c<\/a>), e.g. health-related or medical data.<\/li>
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