Privacy-preserving Record Linkage
A crucial task in data integration is record linkage or entity resolution that aims at linking records that refer to the same real-world entity, such as persons. For example, in multi-site medical research data from several sources (e. g., hospitals) has to be matched to analyze correlations between medical data of the same patient in multiple data sources.
Typically, there is a lack of global identifiers, therefore the linkage can only be achieved by comparing available quasi-identifiers, such as name, address or date of birth. However, in many cases, data owners are only willing or allowed to provide their data for such data integration if there is sufficient protection of sensitive information to ensure the privacy of persons. Privacy-preserving Record Linkage addresses this problem by providing techniques to match records without revealing plaintext data to each other or to third parties |
You can rewatch this lecture on YouTube.
Mit dem Laden des Videos akzeptieren Sie die Datenschutzerklärung von YouTube.
Mehr erfahren
Video laden
Living Lab Lecture Series
The Living Lab Lecture Series gives you an in-depth insight into the many research topics of ScaDS.AI Dresden/Leipzig. From Natural Language Processing to Ethics and Moral Code in AI, a great variety of topics are discussed. You can join our lectures every first thursday of the month or watch them on YouTube afterwards. If you have ideas for topics to discuss in the future, please let our Living Lab team know. We suggest for you to regularly check our event calendar, to never miss out on upcoming lectures or other interesting events organized by or in cooperation with our center.