39 citations · 61 across the 19 of their papers we have counts for
11 papers · 1 filter
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models
Olivia Ma, Jonathan Passerat-Palmbach, Dmitrii Usynin
Fine-tuning large language models (LLMs) for specific tasks introduces privacy risks, as models may inadvertently memorise and leak sensitive training data. While Differential Priv…
SoK: Memorisation in machine learning
Dmitrii Usynin, Moritz Knolle, Georgios Kaissis
Quantifying the impact of individual data samples on machine learning models is an open research problem. This is particularly relevant when complex and high-dimensional relationsh…
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
Obtaining high-quality data for collaborative training of machine learning models can be a challenging task due to A) regulatory concerns and B) a lack of data owner incentives to…
Can collaborative learning be private, robust and scalable?
Dmitrii Usynin, Helena Klause, Johannes C. Paetzold +2
In federated learning for medical image analysis, the safety of the learning protocol is paramount. Such settings can often be compromised by adversaries that target either the pri…
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks
Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
Collaborative machine learning settings like federated learning can be susceptible to adversarial interference and attacks. One class of such attacks is termed model inversion atta…
Differentially Private Graph Classification with GNNs
Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar +3
Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications such as the analysis of social networks, protein inte…