7 research outputs found
A Meaningful MD5 Hash Collision Attack
It is now proved by Wang et al., that MD5 hash is no more secure, after they proposed an attack that would generate two different messages that gives the same MD5 sum. Many conditions need to be satisfied to attain this collision. Vlastimil Klima then proposed a more efficient and faster technique to implement this attack. We use these techniques to first create a collision attack and then use these collisions to implement meaningful collisions by creating two different packages that give identical MD5 hash, but when extracted, each gives out different files with contents specified by the atacker
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Federated benchmarking of medical artificial intelligence with MedPerf
Funder: NIH (U01CA242871) NIH (U24CA189523)AbstractMedical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.</jats:p