A5013666488- Privacy-Preserving Technologies in Data
- Scientific Computing and Data Management
- Data Quality and Management
- Research Data Management Practices
- Cutaneous Melanoma Detection and Management
- Blockchain Technology Applications and Security
- Data Mining Algorithms and Applications
- Digitalization, Law, and Regulation
- Legal, Health, Environmental and COVID-19 Challenges
- Machine Learning in Healthcare
- Big Data and Business Intelligence
- Generative Adversarial Networks and Image Synthesis
University of Cologne
2022-2023
University Hospital Cologne
2022-2023
RWTH Aachen University
2021-2022
In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth health care data. However, data protection regulations prohibit centralisation for analysis purposes because potential privacy risks like accidental disclosure third parties. Therefore, alternative usage policies, which comply with present guidelines, are particular interest.We aim enable analyses on sensitive patient by simultaneously complying...
The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts get access but sensitive due privacy considerations. One solution most these are Distributed Analytics (DA) infrastructures, which technologies fostering collaborations between healthcare institutions by establishing privacy-preserving network sharing. However, in order participate such network, lot technical and administrative...
The exponential growth in data production has led to increasing demand for high-quality data-driven services. Additionally, the benefits of analysis are vast and have significantly propelled research many fields. Data sharing scientific advancement, as it promotes transparency, collaboration, accelerates aids making informed decisions. European strategy aims create a single market that ensures Europe's global competitiveness sovereignty. Common Spaces ensure from different sources available...
The principles of data spaces for sovereign exchange across trusted organizations have so far mainly been adopted in business-to-business settings, and recently scaled to cloud environments. Meanwhile, research established distributed infrastructures, respecting the principle that must be FAIR, i.e., findable, accessible, interoperable reusable. For mutual benefit these two communities, FAIR Data Spaces project aims connect them towards vision a common, cloud-based space industry research....
Data sharing is often met with resistance in medicine and healthcare, due to the sensitive nature heterogeneous characteristics of health data. The lack standardization semantics further exacerbate problems data fragments silos, which makes analytics challenging. NFDI4Health aims develop a infrastructure for personalized research make generated clinical trials, epidemiological, public studies FAIR (Findable, Accessible, Interoperable, Reusable). Since this distributed over various partners...
Data privacy and ownership are significant in social data science, raising legal ethical concerns. Sharing analyzing is difficult when different parties own parts of it. An approach to this challenge apply de-identification or anonymization techniques the before collecting it for analysis. However, can reduce utility increase risk re-identification. To address these limitations, we present PADME, a distributed analytics tool that federates model implementation training. PADME uses federated...
Data privacy and ownership are significant in social data science, raising legal ethical concerns. Sharing analyzing is difficult when different parties own parts of it. An approach to this challenge apply de-identification or anonymization techniques the before collecting it for analysis. However, can reduce utility increase risk re-identification. To address these limitations, we present PADME-SoSci, a distributed analytics tool that federates model implementation training. PADME-SoSci...