A comprehensive survey on poisoning attacks and countermeasures in machine learning

Z Tian, L Cui, J Liang, S Yu - ACM Computing Surveys, 2022 - dl.acm.org
The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

I know what you trained last summer: A survey on stealing machine learning models and defences

D Oliynyk, R Mayer, A Rauber - ACM Computing Surveys, 2023 - dl.acm.org
Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making
even the most complex Machine Learning models available for clients via, eg, a pay-per …

Trustworthy llms: a survey and guideline for evaluating large language models' alignment

Y Liu, Y Yao, JF Ton, X Zhang, R Guo, H Cheng… - ar**
X Cao, M Fang, J Liu, NZ Gong - arxiv preprint arxiv:2012.13995, 2020 - arxiv.org
Byzantine-robust federated learning aims to enable a service provider to learn an accurate
global model when a bounded number of clients are malicious. The key idea of existing …

Data poisoning attacks against federated learning systems

V Tolpegin, S Truex, ME Gursoy, L Liu - … 14–18, 2020, proceedings, part i …, 2020 - Springer
Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …

Challenges in deploying machine learning: a survey of case studies

A Paleyes, RG Urma, ND Lawrence - ACM computing surveys, 2022 - dl.acm.org
In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

Wild patterns reloaded: A survey of machine learning security against training data poisoning

AE Cinà, K Grosse, A Demontis, S Vascon… - ACM Computing …, 2023 - dl.acm.org
The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

Privacy and robustness in federated learning: Attacks and defenses

L Lyu, H Yu, X Ma, C Chen, L Sun… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …