39 citations · 61 across the 19 of their papers we have counts for
19 papers
Biologically-Informed Hybrid Membership Inference Attacks on Generative Genomic Models
Asia Belfiore, Jonathan Passerat-Palmbach, Dmitrii Usynin
The increased availability of genetic data has transformed genomics research, but raised many privacy concerns regarding its handling due to its sensitive nature. This work explore…
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…
Membership Inference Attacks Against Semantic Segmentation Models
Tomas Chobola, Dmitrii Usynin, Georgios Kaissis
Membership inference attacks aim to infer whether a data record has been used to train a target model by observing its predictions. In sensitive domains such as healthcare, this ca…
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Tamara T. Mueller, Stefan Kolek, Friederike Jungmann +5
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their at…