Learning and Sharing under Privacy constraints
Objective
In the DataLEASH project, practically, we develop and test machine learning models, among other methods, to ensure the use of data without the risk of revealing people’s identities or allowing unwanted inferences about them. In a more theoretical approach, we aim at provable guarantees for privacy and take a holistic approach to the legal implications. This implies a quest for finding relevant rules and regulations and illuminating interpretation and application.
The project consortium from KTH, SU, and RISE has a unique set-up in terms of an interdisciplinary and multidisciplinary profile among the researchers, combining perspectives from information theory, legal informatics, language processing, machine learning, cryptography, and systems security.
Background
Digitalization has resulted in more and more data being generated and collected from various sources (such as health care, customer service, surveillance cameras, etc.). The data is valuable for processing and additional analysis to improve predictions and planning. Advances in machine learning have improved this kind of data analysis, while data-protection regulation such as the GDPR has introduced constraints, limiting what data can be used and for what purpose. There is, thus a tension between the utility of data and the privacy of the individuals the data is about.
Cross-disciplinary collaboration
DataLEASH brings together researchers from the School of Electrical Engineering and Computer Science (EECS, KTH), the Department of Computer and Systems Sciences (DSV) and the Department of Law both at Stockholm University and from the Decisions, Network, and Analytics lab at RISE.
Watch the recorded presentation at Digitalize in Stockholm 2022 event:
Contacts
Tobias Oechtering
Professor, Division of Information Science and Engineering, Director Digitalisation Platform at KTH, Member of the Governing Board, Working group Trust, Digital Futures fellow, PI of research project Learning and Sharing under Privacy constraints (DataLEASH), Digital Futures Faculty
+46 8 790 84 62oech@kth.se
Sonja Buchegger
Professor, Division of Theoretical and Computer Science at KTH, Working group Trust, Co-PI of research project Learning and Sharing under Privacy constraints (DataLEASH), Digital Futures Faculty
+46 8 790 62 89buc@kth.se
Hercules Dalianis
Professor Department of Computer and Systems Sciences, Stockholm University, Co-PI of research project Learning and Sharing under Privacy constraints (DataLEASH), Digital Futures Faculty
+46 70 568 13 59hercules@dsv.su.se
Cecilia Magnusson Sjöberg
Professor, LL.D., Department of Law at Stockholm University, Strategic Research Committee, Co-PI of research project Learning and Sharing under Privacy constraints (DataLEASH), Digital Futures Faculty
+46 8 162 893Cecilia.MagnussonSjoberg@juridicum.su.se
Sepideh Pashami
Senior Researcher at Data Analysis Unit, Digital Systems Division at RISE, Co-PI of research project Learning and Sharing under Privacy constraints (DataLEASH) at Digital Futures
+46 10 228 40 73sepideh.pashami@ri.se
Douglas Wikström
Associate Professor, Division of Theoretical Computer Science at KTH, Co-PI of research project Learning and Sharing under Privacy constraints (DataLEASH), Digital Futures Faculty
+46 8 790 81 38dog@kth.se