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Antoine Boutet

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Cryptography and Security Machine Learning

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preprint2026arXiv

Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning

Decentralized learning (DL) is an emerging machine learning paradigm where nodes collaboratively train models without a central server. However, the collaborative nature of DL makes it vulnerable to backdoor attacks, where a model is taught to behave normally on standard inputs while executing hidden, malicious actions when encountering data with specific triggers. Backdoor attacks in DL remain understudied and existing defenses often overlook DL constraints. We introduce Argus, a novel backdoor detection framework native to DL that requires neither a central coordinator nor prior knowledge of the trigger. In Argus, honest nodes locally analyze received model updates to identify potential backdoor triggers. Nodes then collectively share their triggers with their neighbors and use a structural similarity metric to separate true backdoors from false alarms induced by data heterogeneity. A key insight is that false positive triggers exhibit inconsistencies across participants while true positive ones show consistent patterns. Model updates that fail this collaborative test are rejected, and persistently malicious senders are eventually evicted. We provide the first theoretical convergence guarantees for a DL-specific backdoor detection mechanism, showing that filtering out suspicious model updates with high probability preserves a convergence rate comparable to standard DL. We implement and evaluate Argus on three standard datasets and against three state-of-the-art baselines. Across settings, Argus reduces attack success rates by up to 90 points compared to no defense, while preserving model utility within 5 percentage points of an omniscient oracle. Furthermore, the effectiveness of Argus compared to baselines improves as data heterogeneity increases.

preprint2022arXiv

GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning

Embedded systems demand on-device processing of data using Neural Networks (NNs) while conforming to the memory, power and computation constraints, leading to an efficiency and accuracy tradeoff. To bring NNs to edge devices, several optimizations such as model compression through pruning, quantization, and off-the-shelf architectures with efficient design have been extensively adopted. These algorithms when deployed to real world sensitive applications, requires to resist inference attacks to protect privacy of users training data. However, resistance against inference attacks is not accounted for designing NN models for IoT. In this work, we analyse the three-dimensional privacy-accuracy-efficiency tradeoff in NNs for IoT devices and propose Gecko training methodology where we explicitly add resistance to private inferences as a design objective. We optimize the inference-time memory, computation, and power constraints of embedded devices as a criterion for designing NN architecture while also preserving privacy. We choose quantization as design choice for highly efficient and private models. This choice is driven by the observation that compressed models leak more information compared to baseline models while off-the-shelf efficient architectures indicate poor efficiency and privacy tradeoff. We show that models trained using Gecko methodology are comparable to prior defences against black-box membership attacks in terms of accuracy and privacy while providing efficiency.

preprint2022arXiv

Inferring Sensitive Attributes from Model Explanations

Model explanations provide transparency into a trained machine learning model's blackbox behavior to a model builder. They indicate the influence of different input attributes to its corresponding model prediction. The dependency of explanations on input raises privacy concerns for sensitive user data. However, current literature has limited discussion on privacy risks of model explanations. We focus on the specific privacy risk of attribute inference attack wherein an adversary infers sensitive attributes of an input (e.g., race and sex) given its model explanations. We design the first attribute inference attack against model explanations in two threat models where model builder either (a) includes the sensitive attributes in training data and input or (b) censors the sensitive attributes by not including them in the training data and input. We evaluate our proposed attack on four benchmark datasets and four state-of-the-art algorithms. We show that an adversary can successfully infer the value of sensitive attributes from explanations in both the threat models accurately. Moreover, the attack is successful even by exploiting only the explanations corresponding to sensitive attributes. These suggest that our attack is effective against explanations and poses a practical threat to data privacy. On combining the model predictions (an attack surface exploited by prior attacks) with explanations, we note that the attack success does not improve. Additionally, the attack success on exploiting model explanations is better compared to exploiting only model predictions. These suggest that model explanations are a strong attack surface to exploit for an adversary.

preprint2020arXiv

DESIRE: A Third Way for a European Exposure Notification System Leveraging the best of centralized and decentralized systems

This document presents an evolution of the ROBERT protocol that decentralizes most of its operations on the mobile devices. DESIRE is based on the same architecture than ROBERT but implements major privacy improvements. In particular, it introduces the concept of Private Encounter Tokens, that are secret and cryptographically generated, to encode encounters. In the DESIRE protocol, the temporary Identifiers that are broadcast on the Bluetooth interfaces are generated by the mobile devices providing more control to the users about which ones to disclose. The role of the server is merely to match PETs generated by diagnosed users with the PETs provided by requesting users. It stores minimal pseudonymous data. Finally, all data that are stored on the server are encrypted using keys that are stored on the mobile devices, protecting against data breach on the server. All these modifications improve the privacy of the scheme against malicious users and authority. However, as in the first version of ROBERT, risk scores and notifications are still managed and controlled by the server of the health authority, which provides high robustness, flexibility, and efficacy.

preprint2016arXiv

Adaptive Location Privacy with ALP

With the increasing amount of mobility data being collected on a daily basis by location-based services (LBSs) comes a new range of threats for users, related to the over-sharing of their location information. To deal with this issue, several location privacy protection mechanisms (LPPMs) have been proposed in the past years. However, each of these mechanisms comes with different configuration parameters that have a direct impact both on the privacy guarantees offered to the users and on the resulting utility of the protected data. In this context, it can be difficult for non-expert system designers to choose the appropriate configuration to use. Moreover, these mechanisms are generally configured once for all, which results in the same configuration for every protected piece of information. However, not all users have the same behaviour, and even the behaviour of a single user is likely to change over time. To address this issue, we present in this paper ALP, a new framework enabling the dynamic configuration of LPPMs. ALP can be used in two scenarios: (1) offline, where ALP enables a system designer to choose and automatically tune the most appropriate LPPM for the protection of a given dataset; (2) online, where ALP enables the user of a crowd sensing application to protect consecutive batches of her geolocated data by automatically tuning an existing LPPM to fulfil a set of privacy and utility objectives. We evaluate ALP on both scenarios with two real-life mobility datasets and two state-of-the-art LPPMs. Our experiments show that the adaptive LPPM configurations found by ALP outperform both in terms of privacy and utility a set of static configurations manually fixed by a system designer.