6 citations · 8 across the 9 of their papers we have counts for
9 papers
Pseudorandom Functions in from LWE/LPN/CDH (Or: How to Build PRFs in , Generically)
Youlong Ding, Aayush Jain, Ilan Komargodski
We present a new generic transformation from weak PRFs computable in depth to strong PRFs computable in depth . This construction refines the classical…
Linear-Time Verification of Rings and Fields
Youlong Ding
We consider the following problems: Given two tables defining binary operations and on a set of elements, decide whether forms a ring…
SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models
Peihua Mai, Xuanrong Gao, Youlong Ding +3
With the widespread deployment of public large language models (LLMs) such as ChatGPT, protecting user prompt privacy has become an increasingly critical issue. Existing privacy-pr…
SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
Peihua Mai, Youlong Ding, Ziyan Lyu +2
Federated recommender system (FedRec) has emerged as a solution to protect user data through collaborative training techniques. A typical FedRec involves transmitting the full mode…
Delving into Differentially Private Transformer
Youlong Ding, Xueyang Wu, Yining Meng +3
Deep learning with differential privacy (DP) has garnered significant attention over the past years, leading to the development of numerous methods aimed at enhancing model accurac…
PAC Privacy Preserving Diffusion Models
Qipan Xu, Youlong Ding, Xinxi Zhang +2
Data privacy protection is garnering increased attention among researchers. Diffusion models (DMs), particularly with strict differential privacy, can potentially produce images wi…
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data
Youlong Ding, Xueyang Wu, Hao Wang +1
The Transformer has emerged as a versatile and effective architecture with broad applications. However, it still remains an open problem how to efficiently train a Transformer mode…
Revisiting Hyperparameter Tuning with Differential Privacy
Youlong Ding, Xueyang Wu
Hyperparameter tuning is a common practice in the application of machine learning but is a typically ignored aspect in the literature on privacy-preserving machine learning due to…
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Youlong Ding, Xueyang Wu, Zhitao Li +5
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network…