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AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine
  Learning

AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning

29 December 2023
Hideaki Takahashi
    SILM
ArXivPDFHTML

Papers citing "AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning"

4 / 4 papers shown
Title
Opacus: User-Friendly Differential Privacy Library in PyTorch
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour
I. Shilov
Alexandre Sablayrolles
Davide Testuggine
Karthik Prasad
...
Sayan Gosh
Akash Bharadwaj
Jessica Zhao
Graham Cormode
Ilya Mironov
VLM
144
348
0
25 Sep 2021
Label Leakage and Protection in Two-party Split Learning
Label Leakage and Protection in Two-party Split Learning
Oscar Li
Jiankai Sun
Xin Yang
Weihao Gao
Hongyi Zhang
Junyuan Xie
Virginia Smith
Chong-Jun Wang
FedML
122
139
0
17 Feb 2021
Federated Learning: Opportunities and Challenges
Federated Learning: Opportunities and Challenges
P. Mammen
FedML
42
210
0
14 Jan 2021
FedML: A Research Library and Benchmark for Federated Machine Learning
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He
Songze Li
Jinhyun So
Xiao Zeng
Mi Zhang
...
Yang Liu
Ramesh Raskar
Qiang Yang
M. Annavaram
Salman Avestimehr
FedML
162
563
0
27 Jul 2020
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