20 citations · 26 across the 5 of their papers we have counts for
5 papers
MOFHEI: Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference
Parsa Ghazvinian, Robert Podschwadt, Prajwal Panzade +2
Due to the extensive application of machine learning (ML) in a wide range of fields and the necessity of data privacy, privacy-preserving machine learning (PPML) solutions have rec…
RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning
Javad Rafiei Asl, Prajwal Panzade, Eduardo Blanco +2
Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite thei…
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
In today's machine learning landscape, fine-tuning pretrained transformer models has emerged as an essential technique, particularly in scenarios where access to task-aligned train…
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…
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…