2 citations · 6 across the 7 of their papers we have counts for
7 papers
DistilLock: Safeguarding LLMs from Unauthorized Knowledge Distillation on the Edge
Asmita Mohanty, Gezheng Kang, Lei Gao +1
Large Language Models (LLMs) have demonstrated strong performance across diverse tasks, but fine-tuning them typically relies on cloud-based, centralized infrastructures. This requ…
CADC: Encoding User-Item Interactions for Compressing Recommendation Model Training Data
Hossein Entezari Zarch, Abdulla Alshabanah, Chaoyi Jiang +1
Deep learning recommendation models (DLRMs) are at the heart of the current e-commerce industry. However, the amount of training data used to train these large models is growing ex…
Differentially Private Next-Token Prediction of Large Language Models
James Flemings, Meisam Razaviyayn, Murali Annavaram
Ensuring the privacy of Large Language Models (LLMs) is becoming increasingly important. The most widely adopted technique to accomplish this is DP-SGD, which trains a model to gua…
Ethos: Rectifying Language Models in Orthogonal Parameter Space
Lei Gao, Yue Niu, Tingting Tang +2
Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and t…
Edge Private Graph Neural Networks with Singular Value Perturbation
Tingting Tang, Yue Niu, Salman Avestimehr +1
Graph neural networks (GNNs) play a key role in learning representations from graph-structured data and are demonstrated to be useful in many applications. However, the GNN trainin…
CompactTag: Minimizing Computation Overheads in Actively-Secure MPC for Deep Neural Networks
Yongqin Wang, Pratik Sarkar, Nishat Koti +2
Secure Multiparty Computation (MPC) protocols enable secure evaluation of a circuit by several parties, even in the presence of an adversary who maliciously corrupts all but one of…