1 code implementation • 12 Feb 2024 • Hideaki Takahashi, Alex Fukunaga
Concealing an intermediate point on a route or visible from a route is an important goal in some transportation and surveillance scenarios.
1 code implementation • 29 Dec 2023 • Hideaki Takahashi
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.
1 code implementation • 15 Oct 2023 • Tianyuan Zou, Zixuan Gu, Yu He, Hideaki Takahashi, Yang Liu, Ya-Qin Zhang
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.
no code implementations • 19 Jul 2023 • Hideaki Takahashi, Jingjing Liu, Yang Liu
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.
1 code implementation • CVPR 2023 • Hideaki Takahashi, Jingjing Liu, Yang Liu
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.