33 citations · 49 across the 42 of their papers we have counts for
27 papers · 1 filter
TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
Dariush Wahdany, Matthew Jagielski, Jesse C. Cresswell +2
Tabular foundation models enable accurate in-context learning (ICL) from small labeled datasets, but the private records placed in context can leak through model predictions. We fi…
Dataset Usage Inference without Shadow Models or Held-out Data
Wojciech Łapacz, Stanisław Pawlak, Jan Dubiński +2
How much of my data was used to train a machine learning model? Dataset Usage Inference (DUI) aims to answer this by estimating what fraction of a dataset contributed to a model's…
Natural Identifiers for Privacy and Data Audits in Large Language Models
Lorenzo Rossi, Bartłomiej Marek, Franziska Boenisch +1
Assessing the privacy of large language models (LLMs) presents significant challenges. In particular, most existing methods for auditing differential privacy require the insertion…
MGI: Member vs Generated Inference
Bihe Zhao, Michel Meintz, Juangui Xu +2
As generative models increasingly produce samples that are indistinguishable from human-created content, it becomes difficult to determine whether a given data point was part of a…
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
Bartłomiej Marek, Lorenzo Rossi, Vincent Hanke +4
Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees. However, its practical effectiv…
Curation Leaks: Membership Inference Attacks against Data Curation for Machine Learning
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic +1
In machine learning, curation is used to select the most valuable data for improving both model accuracy and computational efficiency. Recently, curation has also been explored as…
On Stealing Graph Neural Network Models
Marcin Podhajski, Jan Dubiński, Franziska Boenisch +3
Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in reality, the number of allowed que…
Memorization in Graph Neural Networks
Adarsh Jamadandi, Jing Xu, Adam Dziedzic +1
Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMem…
Localizing and Mitigating Memorization in Image Autoregressive Models
Aditya Kasliwal, Franziska Boenisch, Adam Dziedzic
Image AutoRegressive (IAR) models have achieved state-of-the-art performance in speed and quality of generated images. However, they also raise concerns about memorization of their…
Radioactive Watermarks in Diffusion and Autoregressive Image Generative Models
Michel Meintz, Jan Dubiński, Franziska Boenisch +1
Image generative models have become increasingly popular, but training them requires large datasets that are costly to collect and curate. To circumvent these costs, some parties m…
Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
Xun Wang, Jing Xu, Franziska Boenisch +3
Prompting has become a dominant paradigm for adapting large language models (LLMs). While discrete (textual) prompts are widely used for their interpretability, soft (parameter) pr…
Unlocking Post-hoc Dataset Inference with Synthetic Data
Bihe Zhao, Pratyush Maini, Franziska Boenisch +1
The remarkable capabilities of Large Language Models (LLMs) can be mainly attributed to their massive training datasets, which are often scraped from the internet without respectin…
DP-GPL: Differentially Private Graph Prompt Learning
Jing Xu, Franziska Boenisch, Iyiola Emmanuel Olatunji +1
Graph Neural Networks (GNNs) have shown remarkable performance in various applications. Recently, graph prompt learning has emerged as a powerful GNN training paradigm, inspired by…
Differentially Private Federated Learning With Time-Adaptive Privacy Spending
Shahrzad Kiani, Nupur Kulkarni, Adam Dziedzic +2
Federated learning (FL) with differential privacy (DP) provides a framework for collaborative machine learning, enabling clients to train a shared model while adhering to strict pr…
On the Privacy Risk of In-context Learning
Haonan Duan, Adam Dziedzic, Mohammad Yaghini +2
Large language models (LLMs) are excellent few-shot learners. They can perform a wide variety of tasks purely based on natural language prompts provided to them. These prompts cont…
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
Vincent Hanke, Tom Blanchard, Franziska Boenisch +3
While open Large Language Models (LLMs) have made significant progress, they still fall short of matching the performance of their closed, proprietary counterparts, making the latt…
CDI: Copyrighted Data Identification in Diffusion Models
Jan Dubiński, Antoni Kowalczuk, Franziska Boenisch +1
Diffusion Models (DMs) benefit from large and diverse datasets for their training. Since this data is often scraped from the Internet without permission from the data owners, this…
Localizing Memorization in SSL Vision Encoders
Wenhao Wang, Adam Dziedzic, Michael Backes +1
Recent work on studying memorization in self-supervised learning (SSL) suggests that even though SSL encoders are trained on millions of images, they still memorize individual data…
LLM Dataset Inference: Did you train on my dataset?
Pratyush Maini, Hengrui Jia, Nicolas Papernot +1
The proliferation of large language models (LLMs) in the real world has come with a rise in copyright cases against companies for training their models on unlicensed data from the…
Differentially Private Prototypes for Imbalanced Transfer Learning
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic +1
Machine learning (ML) models have been shown to leak private information from their training datasets. Differential Privacy (DP), typically implemented through the differential pri…
MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation
Yihan Wang, Yiwei Lu, Guojun Zhang +4
Machine unlearning offers effective solutions for revoking the influence of specific training data on pre-trained model parameters. While existing approaches address unlearning for…
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
Dominik Hintersdorf, Lukas Struppek, Kristian Kersting +2
Diffusion models (DMs) produce very detailed and high-quality images. Their power results from extensive training on large amounts of data, usually scraped from the internet withou…
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
Marcin Podhajski, Jan Dubiński, Franziska Boenisch +3
Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which allow for the processing…
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
Congyu Fang, Adam Dziedzic, Lin Zhang +5
Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healt…