9 citations · 21 across the 6 of their papers we have counts for
6 papers
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator
Javad Rafiei Asl, Mohammad H. Rafiei, Manar Alohaly +1
Machine learning models are vulnerable to maliciously crafted Adversarial Examples (AEs). Training a machine learning model with AEs improves its robustness and stability against a…
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…
An Insider Threat Mitigation Framework Using Attribute Based Access Control
Olusesi Balogun, Daniel Takabi
Insider Threat is a significant and potentially dangerous security issue in corporate settings. It is difficult to mitigate because, unlike external threats, insiders have knowledg…
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption
Robert Podschwadt, Daniel Takabi, Peizhao Hu
Outsourced computation for neural networks allows users access to state of the art models without needing to invest in specialized hardware and know-how. The problem is that the us…