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Found 7 papers, 1 papers with code
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator
SSCAE outperforms the existing models in all experiments while maintaining a higher semantic consistency with a lower query number and a comparable perturbation rate.
RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning
In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks.
I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption
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 training data is limited.
MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption
Advancements in machine learning (ML) have significantly revolutionized medical image analysis, prompting hospitals to rely on external ML services.
SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges
There are approaches based on fully homomorphic encryption (FHE), secure multiparty computation (SMC), and, more recently, functional encryption (FE).
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption
Outsourced computation for neural networks allows users access to state of the art models without needing to invest in specialized hardware and know-how.
Privacy preserving Neural Network Inference on Encrypted Data with GPUs
Machine Learning as a Service (MLaaS) has become a growing trend in recent years and several such services are currently offered.
