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cs.CR2024★ 2 cited
MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption
Prajwal Panzade, Daniel Takabi, Zhipeng Cai
Advancements in machine learning (ML) have significantly revolutionized medical image analysis, prompting hospitals to rely on external ML services. However, the exchange of sensit…
cs.CR2022★ 20 cited
SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges
Prajwal Panzade, Daniel Takabi
With the advent of functional encryption, new possibilities for computation on encrypted data have arisen. Functional Encryption enables data owners to grant third-party access to…