5 papers
Learning from Anonymized and Incomplete Tabular Data
Lucas Lange, Adrian Böttinger, Victor Christen +3
User-driven privacy allows individuals to control whether and at what granularity their data is shared, leading to datasets that mix original, generalized, and missing values withi…
KGpipe: Generation and Evaluation of Pipelines for Data Integration into Knowledge Graphs
Marvin Hofer, Erhard Rahm
Building high-quality knowledge graphs (KGs) from diverse sources requires combining methods for information extraction, data transformation, ontology mapping, entity matching, and…
Federated Learning With Individualized Privacy Through Client Sampling
Lucas Lange, Ole Borchardt, Erhard Rahm
With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privac…
Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning
Lucas Lange, Maurice-Maximilian Heykeroth, Erhard Rahm
Machine Learning (ML) is crucial in many sectors, including computer vision. However, ML models trained on sensitive data face security challenges, as they can be attacked and leak…
Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure
Florens Rohde, Victor Christen, Martin Franke +1
Privacy-Preserving Record linkage (PPRL) is an essential component in data integration tasks of sensitive information. The linkage quality determines the usability of combined data…