208 citations · 588 across the 22 of their papers we have counts for
13 papers · 1 filter
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
Georgios Kaissis, Jamie Hayes, Alexander Ziller +1
We explore Reconstruction Robustness (ReRo), which was recently proposed as an upper bound on the success of data reconstruction attacks against machine learning models. Previous r…
Bounding Training Data Reconstruction in DP-SGD
Jamie Hayes, Saeed Mahloujifar, Borja Balle
Differentially private training offers a protection which is usually interpreted as a guarantee against membership inference attacks. By proxy, this guarantee extends to other thre…
Extracting Training Data from Diffusion Models
Nicholas Carlini, Jamie Hayes, Milad Nasr +6
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images. In this w…
Reconstructing Training Data with Informed Adversaries
Borja Balle, Giovanni Cherubin, Jamie Hayes
Given access to a machine learning model, can an adversary reconstruct the model's training data? This work studies this question from the lens of a powerful informed adversary who…
Adaptive Webpage Fingerprinting from TLS Traces
Vasilios Mavroudis, Jamie Hayes
In webpage fingerprinting, an on-path adversary infers the specific webpage loaded by a victim user by analysing the patterns in the encrypted TLS traffic exchanged between the use…
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning
Mohammad Naseri, Jamie Hayes, Emiliano De Cristofaro
Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, thi…
Contamination Attacks and Mitigation in Multi-Party Machine Learning
Jamie Hayes, Olga Ohrimenko
Machine learning is data hungry; the more data a model has access to in training, the more likely it is to perform well at inference time. Distinct parties may want to combine thei…
A note on hyperparameters in black-box adversarial examples
Jamie Hayes
Since Biggio et al. (2013) and Szegedy et al. (2013) first drew attention to adversarial examples, there has been a flood of research into defending and attacking machine learning…
Learning Universal Adversarial Perturbations with Generative Models
Jamie Hayes, George Danezis
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a miscla…
LOGAN: Membership Inference Attacks Against Generative Models
Jamie Hayes, Luca Melis, George Danezis +1
Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inf…
The Loopix Anonymity System
Ania Piotrowska, Jamie Hayes, Tariq Elahi +2
We present Loopix, a low-latency anonymous communication system that provides bi-directional 'third-party' sender and receiver anonymity and unobservability. Loopix leverages cover…
Traffic Confirmation Attacks Despite Noise
Jamie Hayes
We propose a traffic confirmation attack on low-latency mix networks based on computing robust real-time binary hashes of network traffic flows. Firstly, we adapt the Coskun-Memon…
k-fingerprinting: a Robust Scalable Website Fingerprinting Technique
Jamie Hayes, George Danezis
Website fingerprinting enables an attacker to infer which web page a client is browsing through encrypted or anonymized network connections. We present a new website fingerprinting…