Towards Faster Functional Encryption for Privacy-preserving Machine Learning. - Prajwal Panzade; Daniel Takabi (TPS-ISA, 2021)

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges. - Prajwal Panzade; Daniel Takabi (CoRR, 2022)

FENet: Privacy-preserving Neural Network Training with Functional Encryption. - Prajwal Panzade; Daniel Takabi (IWSPA@CODASPY, 2023)

RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning. - Javad Rafiei Asl; Prajwal Panzade; Eduardo Blanco 002; Daniel Takabi; Zhipeng Cai 001 (NAACL-HLT (Findings), 2024)

MOFHEI: Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference. - Parsa Ghazvinian; Robert Podschwadt; Prajwal Panzade; Mohammad Hossein Rafiei; Daniel Takabi (TPS-ISA, 2024)

MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption. - Prajwal Panzade; Daniel Takabi; Zhipeng Cai 001 (CoRR, 2024)

I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption. - Prajwal Panzade; Daniel Takabi; Zhipeng Cai 001 (CoRR, 2024)

Privacy-Preserving Machine Learning Using Functional Encryption: Opportunities and Challenges. - Prajwal Panzade; Daniel Takabi; Zhipeng Cai 001 (IEEE Internet Things J., 2024)