publications

2023

1. Bayes Security: A Not So Average Metric Chatzikokolakis, Konstantinos, Cherubin, Giovanni, Palamidessi, Catuscia, and Troncoso, Carmela In 2023 IEEE 36th Computer Security Foundations Symposium (CSF) 2023 [Paper]
2. Approximating full conformal prediction at scale via influence functions Martinez, Javier Abad, Bhatt, Umang, Weller, Adrian, and Cherubin, Giovanni In Proceedings of the AAAI Conference on Artificial Intelligence 2023 [Paper]
3. How do the performance of a Conformal Predictor and its underlying algorithm relate? Cherubin, Giovanni In Conformal and Probabilistic Prediction with Applications 2023 [Paper]
4. SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning Salem, Ahmed, Cherubin, Giovanni, Evans, David, Koepf, Boris, Paverd, Andrew, Suri, Anshuman, Tople, Shruti, and Zanella-Beguelin, Santiago In 2023 IEEE Symposium on Security and Privacy (SP) 2023 [Paper]

2022

1. Disparate vulnerability: On the unfairness of privacy attacks against machine learning Kulynych, Bogdan, Yaghini, Mohammad, Cherubin, M, Veale, G, and Troncoso, C 2022
2. Online Website Fingerprinting: Evaluating Website Fingerprinting Attacks on Tor in the Real World Cherubin, Giovanni, Jansen, Rob, and Troncoso, Carmela In 31st USENIX Security Symposium (USENIX Security 22) 2022 [Paper] [Url]
3. Reconstructing Training Data with Informed Adversaries Balle, Borja, Cherubin, Giovanni, and Hayes, Jamie In 2022 IEEE Symposium on Security and Privacy (SP) 2022 [Paper]
4. Synthetic Data-what, why and how? Jordon, James, Szpruch, Lukasz, Houssiau, Florimond, Bottarelli, Mirko, Cherubin, Giovanni, Maple, Carsten, Cohen, Samuel N, and Weller, Adrian Royal Society 2022 [Paper]

2021

1. Exact Optimization of Conformal Predictors via Incremental and Decremental Learning Cherubin, Giovanni, Chatzikokolakis, Konstantinos, and Jaggi, Martin In Proceedings of the 38th International Conference on Machine Learning 2021 [Abs] [Paper] [Url]

   Conformal Predictors (CP) are wrappers around ML models, providing error guarantees under weak assumptions on the data distribution. They are suitable for a wide range of problems, from classification and regression to anomaly detection. Unfortunately, their very high computational complexity limits their applicability to large datasets. In this work, we show that it is possible to speed up a CP classifier considerably, by studying it in conjunction with the underlying ML method, and by exploiting incremental&decremental learning. For methods such as k-NN, KDE, and kernel LS-SVM, our approach reduces the running time by one order of magnitude, whilst producing exact solutions. With similar ideas, we also achieve a linear speed up for the harder case of bootstrapping. Finally, we extend these techniques to improve upon an optimization of k-NN CP for regression. We evaluate our findings empirically, and discuss when methods are suitable for CP optimization.

2. (Poster) Fast conformal classification using influence functions Bhatt, Umang, Weller, Adrian, and Cherubin, Giovanni In Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications 2021 [Abs] [Paper] [Url]

   We use influence functions from robust statistics to speed up full conformal prediction. Traditionally, conformal prediction requires retraining multiple leave-one-out classifiers to calculate p-values for each test point. By using influence functions, we are able to approximate this procedure and to speed up considerably the time complexity.

3. Reconstructing Training Data with Informed Adversaries Balle, Borja, Cherubin, Giovanni, and Hayes, Jamie In NeurIPS 2021 Workshop Privacy in Machine Learning 2021 [Paper] [Url]

2020

2019

1. Black-box Security: Measuring Black-box Information Leakage via Machine Learning Cherubin, Giovanni PhD thesis 2019 [PDF]
2. F-BLEAU: Fast Black-box Leakage Estimation Cherubin, Giovanni, Chatzikokolakis, Konstantinos, and Palamidessi, Catuscia In IEEE Symposium on Security and Privacy (S&P) 2019 [Abs] [Paper] [Video]

   We consider the problem of measuring how much a system reveals about its secret inputs. We work under the black-box setting: we assume no prior knowledge of the system’s internals, and we run the system for choices of secrets and measure its leakage from the respective outputs. Our goal is to estimate the Bayes risk, from which one can derive some of the most popular leakage measures (eg, min-entropy, additive, and multiplicative leakage). The state-of-the-art method for estimating these leakage measures is the frequentist paradigm, which approximates the system’s internals by looking at the frequencies of its inputs and outputs. Unfortunately, this does not scale for systems with large output spaces, where it would require too many input-output examples. Consequently, it also cannot be applied to systems with continuous outputs (eg, time side channels, network traffic).

2018

1. Exchangeability martingales for selecting features in anomaly detection Cherubin, Giovanni, Baldwin, Adrian, and Griffin, Jonathan In Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications 2018 [Abs] [Paper] [Url] [Slides] [Code]

   We consider the problem of feature selection for unsupervised anomaly detection (AD) in time-series, where only normal examples are available for training. We develop a method based on exchangeability martingales that only keeps features that exhibit the same pattern (i.e., are i.i.d.) under normal conditions of the observed phenomenon. We apply this to the problem of monitoring a Windows service and detecting anomalies it exhibits if compromised; results show that our method: i) strongly improves the AD system’s performance, and ii) it reduces its computational complexity. Furthermore, it gives results that are easy to interpret for analysts, and it potentially increases robustness against AD evasion attacks.

2. Majority vote ensembles of conformal predictors Cherubin, Giovanni Machine Learning 2018 [Paper] [Url]

2017

1. Website Fingerprinting Defenses at the Application Layer Cherubin, Giovanni, Hayes, Jamie, and Juarez, Marc Proceedings on Privacy Enhancing Technologies 2017 [Abs] [Paper] [Code]

   We propose two application-layer Website Fingerprinting defences: one server-side (ALPaCA), and one client-side (LLaMA). Both are easy to deploy, and indeed we are currently implementing ALPaCA as an Apache and Nginx module (https://github.com/camelids/libalpaca).

2. Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses Cherubin, Giovanni Proceedings on Privacy Enhancing Technologies 2017 Best student paper [Paper] [Slides] [Code] [Video]

2016

1. Hidden Markov Models with Confidence Cherubin, Giovanni, and Nouretdinov, Ilia In Conformal and Probabilistic Prediction with Applications - 5th International Symposium, COPA 2016 [Paper] [Slides] [Code]

2015

1. Conformal Clustering and Its Application to Botnet Traffic Cherubin, Giovanni, Nouretdinov, Ilia, Gammerman, Alexander, Jordaney, Roberto, Wang, Zhi, Papini, Davide, and Cavallaro, Lorenzo In Statistical Learning and Data Sciences (SLDS) 2015 Best student paper [Paper] [Slides]

2014

1. Bots detection by Conformal Clustering Cherubin, Giovanni MSc thesis, Royal Holloway University of London 2014 [PDF]