4 papers
Secure and Efficient -Norm Computation for Two-Party Learning Applications
Ali Arastehfard, Weiran Liu, Joshua Lee +3
Secure norm computation is becoming increasingly important in many real-world learning applications. However, existing cryptographic systems often lack a general framework for secu…
Efficient and High-Accuracy Secure Two-Party Protocols for a Class of Functions with Real-number Inputs
Hao Guo, Zhaoqian Liu, Liqiang Peng +4
In two-party secret sharing scheme, values are typically encoded as unsigned integers , whereas real-world applications often require computations on signed real…
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang +4
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning en…
From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy
Qingqing Ye, Liantong Yu, Kai Huang +3
Local Differential Privacy (LDP) is the predominant privacy model for safeguarding individual data privacy. Existing perturbation mechanisms typically require perturbing the origin…