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Found 5 papers, 4 papers with code
On the Transit Obfuscation Problem
Concealing an intermediate point on a route or visible from a route is an important goal in some transportation and surveillance scenarios.
AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
This paper introduces AIJack, an open-source library designed to assess security and privacy risks associated with the training and deployment of machine learning models.
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters.
Eliminating Label Leakage in Tree-Based Vertical Federated Learning
To counteract label leakage from the instance space, we propose two effective defense mechanisms, Grafting-LDP, which improves the utility of label differential privacy with post-processing, and andID-LMID, which focuses on mutual information regularization.
Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack
Federated Learning with Model Distillation (FedMD) is a nascent collaborative learning paradigm, where only output logits of public datasets are transmitted as distilled knowledge, instead of passing on private model parameters that are susceptible to gradient inversion attacks, a known privacy risk in federated learning.
