data confidentiality in research and society: what, why and how
The Flames Annual Meeting is an annual one-day symposium targeting hot topics of relevance to society and the place of statistics and methodology therein. Prominent speakers from academia, non-profit and industry, as well as policy makers will share their insights. The morning session will begin with keynote lectures and concludes with a debate around the core subject. The afternoon session is reserved for a workshop catering specifically for young researchers on selected topics.
Data confidentiality pertains to the protection of information that an individual has disclosed in a relationship of trust against unauthorized access, disclosure or theft. Faced with a remarkable increase in the number of data breaches (4 billion last year), there is a need to reinstitute trust in individual patients, customers and the general public. The aim of FAM 2021 is to bring together Data Protection Officers (DPO’s) of both public and private institutions, representatives of ethical committees and researchers, to discuss and exchange experiences about issues tied to data confidentiality. The keynote lecture will offer an overview of data confidentiality including examples of data breaches and some solutions and recommendations. This will be followed by a series of lectures on two main themes: The first theme is about Experiences since the implementation of the GDPR including talks on “maintaining data confidentiality” with speakers from both public and private institutions, The second theme will be on Data Sharing including lectures on “Guidelines on Anonymity and Pseudonymization in Research”. Through a debate, we shall offer a more interactive and in-depth discussion on challenges, solutions and future perspectives of data confidentiality in the era of big data amidst an increasing demand for data sharing. The afternoon session involves working in small groups chaired by experts on topics such as: anonymity and pseudonymization, merging own research data with protected data, analysis of grouped data, and data confidentiality do’s and don’ts.