arXiv subjects

Romaric Duvignau

Publications and source records attributed to Romaric Duvignau.

10 recordsLinked to original sources

arXiv2026

Securing High-Performance Data Transfers: Implementing AES Encryption in RDMA Systems

Remote Direct Memory Access (RDMA) is a key enabler of high-performance systems, offering low latency, high throughput, and reduced CPU overhead by allowing direct memory-to-memory transfers between machines. However, its design bypasses traditional CPU-mediated security mechanisms, introducing critical vulnerabilities in untrusted environments. This work explores the integration of RDMA and AES-128 encryption to secure data transfers without compromising performance. We implement encryption directly within the data plane of a programmable Tofino switch using the P4 programming language. By offloading encryption from the CPU to the switch, our design preserves RDMA's performance benefits while addressing its security shortcomings. Experimental results show that the system achieves throughput of 0.37 Gbps for 16-byte packets, 0.76 Gbps for 32-byte packets, 1.83 Gbps for 64-byte packets, and 1.9 Gbps for 128-byte packets. These findings demonstrate the feasibility of secure, high-throughput RDMA communication using programmable network hardware.

cs.CR↗

arXiv2026

Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods. While various system-level and algorithmic optimizations have been proposed to accelerate Graph Neural Network (GNN) pipelines, data management and movement remain the primary bottlenecks at scale. In this paper, we explore whether graph sparsification, a well-established technique that reduces edges to create sparser neighborhoods, can serve as a lightweight pre-processing step to address these bottlenecks while preserving accuracy on node classification tasks. We develop an extensible experimental framework that enables systematic evaluation of how different sparsification methods affect the performance and accuracy of GNN models. We conduct the first comprehensive study of GNN training and inference on sparsified graphs, revealing several key findings. First, sparsification often preserves or even improves predictive performance. As an example, random sparsification raises the accuracy of the GAT model by 6.8% on the PubMed graph. Second, benefits increase with scale, substantially accelerating both training and inference. Our results show that the K-Neighbor sparsifier improves model serving performance on the Products graph by 11.7x with only a 0.7% accuracy drop. Importantly, we find that the computational overhead of sparsification is quickly amortized, making it practical for very large graphs.

cs.LG↗

arXiv2026

The TCF doesn't really A(A)ID -- Automatic Privacy Analysis and Legal Compliance of TCF-based Android Applications

The Transparency and Consent Framework (TCF), developed by the Interactive Advertising Bureau (IAB) Europe, provides a de facto standard for requesting, recording, and managing user consent from European end-users. This framework has previously been found to infringe European data protection law and has subsequently been regularly updated. Previous research on the TCF focused exclusively on web contexts, with no attention given to its implementation in mobile applications. No work has systematically studied the privacy implications of the TCF on Android apps. To address this gap, we investigate the prevalence of the TCF in popular Android apps from the Google Play Store, and assess whether these apps respect users' consent banner choices. By scraping and downloading 4482 of the most popular Google Play Store apps on an emulated Android device, we automatically determine which apps use the TCF, automatically interact with consent banners, and analyze the apps' traffic in two different stages, passive (post choices) and active (during banner interaction and post choices). We found that 576 (12.85%) of the 4482 downloadable apps in our dataset implemented the TCF, and we identified potential privacy violations within this subset. In 15 (2.6%) of these apps, users' choices are stored only when consent is granted. Users who refuse consent are shown the consent banner again each time they launch the app. Network traffic analysis conducted during the passive stage reveals that 66.2% of the analyzed TCF-based apps share personal data, through the Android Advertising ID (AAID), in the absence of a lawful basis for processing. 55.3% of apps analyzed during the active stage share AAID before users interact with the apps' consent banners, violating the prior consent requirement.

cs.CR↗

arXiv2025

How Feasible are Passive Network Attacks on 5G Networks and Beyond? A Survey

Privacy concerns around 5G, the latest generation of mobile networks, are growing, with fears that its deployment may increase exposure to privacy risks. This perception is largely driven by the use of denser deployments of small antenna systems, which enable highly accurate data collection at higher speeds and closer proximity to mobile users. At the same time, 5G's unique radio communication features can make the reproduction of known network attacks more challenging. In particular, passive network attacks, which do not involve direct interaction with the target network and are therefore nearly impossible to detect, remain a pressing concern. Such attacks can reveal sensitive information about users, their devices, and active applications, which may then be exploited through known vulnerabilities or spear-phishing schemes. This survey examines the feasibility of passive network attacks in 5G and beyond (B5G/6G) networks, with emphasis on two major categories: information extraction (system identification, website and application fingerprinting) and geolocation (user identification and position tracking). These attacks are well documented and reproducible in existing wireless and mobile systems, including short-range networks (IEEE 802.11) and, to a lesser extent, LTE. Current evidence suggests that while such attacks remain theoretically possible in 5G, their practical execution is significantly constrained by directional beamforming, high-frequency propagation characteristics, and encryption mechanisms. For B5G and early 6G networks, the lack of public tools and high hardware cost currently renders these attacks infeasible in practice, which highlights a critical gap in our understanding of future network threat models.

cs.NI↗

arXiv2024

Greediness is not always a vice: Efficient Discovery Algorithms for Assignment Problems

Finding a maximum-weight matching is a classical and well-studied problem in computer science, solvable in cubic time in general graphs. We consider the specialization called assignment problem where the input is a bipartite graph, and introduce in this work the ``discovery'' variant considering edge weights that are not provided as input but must be queried, requiring additional and costly computations. We develop here discovery algorithms aiming to minimize the number of queried weights while providing guarantees on the computed solution. We first show in this work the inherent challenges of designing discovery algorithms for general assignment problems. We then provide and analyze several efficient greedy algorithms that can make use of natural assumptions about the order in which the nodes are processed by the algorithms. Our motivations for exploring this problem stem from finding practical solutions to a variation of maximum-weight matching in bipartite hypergraphs, a problem recently emerging in the formation of peer-to-peer energy sharing communities.

cs.DS↗

arXiv2024

Geographical Peer Matching for P2P Energy Sharing

Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. Such distributed resources are not used in the most effective way when only used individually, as sharing them provides even greater cost savings. Energy Peer-to-Peer (P2P) systems have thus been shown to be beneficial for prosumers and consumers through reductions in energy cost while also being attractive to grid or service providers. However, many practical challenges have to be overcome before all players could gain in having efficient and automated local energy communities; such challenges include the inherent complexity of matching together geographically distributed peers and the significant computations required to calculate the local matching preferences. Hence dedicated algorithms are required to be able to perform a cost-efficient matching of thousands of peers in a computational-efficient fashion. We define and analyze in this work a precise mathematical modelling of the geographical peer matching problem and several heuristics solving it. Our experimental study, based on real-world energy data, demonstrates that our solutions are efficient both in terms of cost savings achieved by the peers and in terms of communication and computing requirements. Our scalable algorithms thus provide one core building block for practical and data-efficient peer-to-peer energy sharing communities within large-scale optimization systems.

cs.ET↗

arXiv2023

Self-stabilizing Byzantine Multivalued Consensus

Consensus, abstracting a myriad of problems in which processes have to agree on a single value, is one of the most celebrated problems of fault-tolerant distributed computing. Consensus applications include fundamental services for the environments of the Cloud and Blockchain, and in such challenging environments, malicious behaviors are often modeled as adversarial Byzantine faults. At OPODIS 2010, Mostefaoui and Raynal (in short MR) presented a Byzantine-tolerant solution to consensus in which the decided value cannot be a value proposed only by Byzantine processes. MR has optimal resilience coping with up to t < n/3 Byzantine nodes over n processes. MR provides this multivalued consensus object (which accepts proposals taken from a finite set of values) assuming the availability of a single Binary consensus object (which accepts proposals taken from the set {0,1}). This work, which focuses on multivalued consensus, aims at the design of an even more robust solution than MR. Our proposal expands MR's fault-model with self-stabilization, a vigorous notion of fault-tolerance. In addition to tolerating Byzantine, self-stabilizing systems can automatically recover after the occurrence of arbitrary transient-faults. These faults represent any violation of the assumptions according to which the system was designed to operate (provided that the algorithm code remains intact). To the best of our knowledge, we propose the first self-stabilizing solution for intrusion-tolerant multivalued consensus for asynchronous message-passing systems prone to Byzantine failures. Our solution has a O(t) stabilization time from arbitrary transient faults.

cs.DC↗

arXiv2022

Self-stabilizing Byzantine Fault-tolerant Repeated Reliable Broadcast

We study a well-known communication abstraction called Byzantine Reliable Broadcast (BRB). This abstraction is central in the design and implementation of fault-tolerant distributed systems, as many fault-tolerant distributed applications require communication with provable guarantees on message deliveries. Our study focuses on fault-tolerant implementations for message-passing systems that are prone to process-failures, such as crashes and malicious behavior. At PODC 1983, Bracha and Toueg, in short, BT, solved the BRB problem. BT has optimal resilience since it can deal with t < n/3 Byzantine processes, where n is the number of processes. The present work aims at the design of an even more robust solution than BT by expanding its fault-model with self-stabilization, a vigorous notion of fault-tolerance. In addition to tolerating Byzantine and communication failures, self-stabilizing systems can recover after the occurrence of arbitrary transient-faults. These faults represent any violation of the assumptions according to which the system was designed to operate (provided that the algorithm code remains intact). We propose, to the best of our knowledge, the first self-stabilizing Byzantine fault-tolerant (BFT) solution for repeated BRB in signature-free message-passing systems (that follows BT's problem specifications). Our contribution includes a self-stabilizing variation on a BT that solves a single-instance BRB for asynchronous systems. We also consider the problem of recycling instances of single-instance BRB. Our self-stabilizing BFT recycling for time-free systems facilitates the concurrent handling of a predefined number of BRB invocations and, by this way, can serve as the basis for self-stabilizing BFT consensus.

cs.DC↗

arXiv2022

Self-stabilizing Byzantine- and Intrusion-tolerant Consensus

One of the most celebrated problems of fault-tolerant distributed computing is the consensus problem. It was shown to abstract a myriad of problems in which processes have to agree on a single value. Consensus applications include fundamental services for the environments of the Cloud or Blockchain. In such challenging environments, malicious behavior is often modeled as adversarial Byzantine faults. At OPODIS 2010, Moste}faoui and Raynal, in short, MR, presented a Byzantine- and intrusion-tolerant solution to consensus in which the decided value cannot be a value proposed only by Byzantine processes. In addition to this validity property, MR has optimal resilience since it can deal with up to t < n/3 Byzantine processes, where n is the number of processes. We note that MR provides this multivalued consensus object (which accepts proposals taken from a set with a finite number of values) assuming the availability of a single Binary consensus object (which accepts proposals taken from the set {0,1}). This work, which focuses on multivalued consensus, aims at the design of an even more robust solution than MR. Our proposal expands MR's fault-model with self-stabilization, a vigorous notion of fault-tolerance. In addition to tolerating Byzantine and communication failures, self-stabilizing systems can automatically recover after the occurrence of arbitrary transient-faults. These faults represent any violation of the assumptions according to which the system was designed to operate (provided that the algorithm code remains intact). To the best of our knowledge, we propose the first self-stabilizing solution for intrusion-tolerant multivalued consensus for asynchronous message-passing systems prone to Byzantine failures.

cs.DC↗

arXiv2018

Piecewise Linear Approximation in Data Streaming: Algorithmic Implementations and Experimental Analysis

Piecewise Linear Approximation (PLA) is a well-established tool to reduce the size of the representation of time series by approximating the series by a sequence of line segments while keeping the error introduced by the approximation within some predetermined threshold. With the recent rise of edge computing, PLA algorithms find a complete new set of applications with the emphasis on reducing the volume of streamed data. In this study, we identify two scenarios set in a data-stream processing context: data reduction in sensor transmissions and datacenter storage. In connection to those scenarios, we identify several streaming metrics and propose streaming protocols as algorithmic implementations of several state of the art PLA techniques. In an experimental evaluation, we measure the quality of the reviewed methods and protocols and evaluate their performance against those streaming statistics. All known methods have deficiencies when it comes to handling streaming-like data, e.g. inflation of the input stream, high latency or poor average error. Our experimental results highlight the challenges raised when transferring those classical methods into the stream processing world and present alternative techniques to overcome them and balance the related trade-offs.

cs.DC↗