arXiv subjects

Pascal Felber

Publications and source records attributed to Pascal Felber.

At least 19 recordsLinked to original sources

arXiv2026

Exploring the Power Consumption of ERC-4337 Bundling on Server Infrastructure

The Ethereum ecosystem offers the most widespread support for Decentralised Applications (DApps), with over 120 networks currently documented as using variants of the Ethereum Virtual Machine (EVM) for core execution logic. The ERC-4337 standard provides convenient access to DApps for users who do not possess the required cryptocurrency or prefer flexible authorisation of DApp actions. The infrastructure for ERC-4337 implementation can be deployed on any EVM network. However, to aggregate user requests and submit them to the EVM network, ERC-4337 requires operating a bundler, an additional component. Recently, the potential use of ERC-4337 was boosted by another standard, EIP-7702, which can enable ERC-4337 utility for any existing user account. However, the use of the combined EIP-7702/ERC-4337 pattern remains considerably low at less than 6% of EIP-7702 activity. We believe that this underutilisation is at least partly caused by the low number of existing bundling services and their popularity, currently fewer than 15 publicly documented. Expanding the number of bundler nodes would benefit the ecosystem but would introduce operational costs, part of which is represented by the added nodes' power consumption. In this paper, we provide a thorough view of the bundling power consumption aspect using server-grade infrastructure, evaluating both CPU and RAM consumption, as well as the overhead of container-based deployments specific to production environments.

cs.NI↗

arXiv2026

Empirical Analysis of TEE-Enabled Rollup Designs

Layer 2 (L2) rollups have become the dominant scaling solution in Ethereum. At the core of these systems lies the rollup sequencer, a component that orders, executes, and submits L2 transaction blocks to the main Ethereum blockchain. The centralization of this sequencer introduces security risks, including censorship and malicious manipulation of transactions to extract maximum extractable value (MEV). Trusted execution environments (TEEs) provide strong hardware-enforced confidentiality and integrity guarantees for code and data. TEEs are strong candidates for strengthening security and ensuring verifiable neutrality in rollup sequencer operations, but multiple TEE-based designs are possible, offering different trade-offs among modularity, performance, and security guarantees. Among them, the separation of proposer and builder logic (PBS) into independent services is a strong contender against the monolithic approach that bundles all sequencer logic. Despite several proposed theoretical frameworks, there is a lack of experimental evaluation of the practicality and security trade-offs of integrating TEEs into rollup sequencers. This work addresses this gap through a comprehensive empirical study of TEE-enabled rollup designs under both PBS and monolithic sequencer models. Our experimental evaluations show that the impact of TEEs on rollup performance is small and that PBS does not significantly amplify it. We also show that PBS can lead to significant performance improvements by enabling more specialized, efficient builder logic implementations.

cs.CR↗

arXiv2026

Early-Stopping Thresholds for ES-HyperNEAT: A Data-Driven Approach from Fitness Dynamics

Most hyperparameter configurations for Evolvable-Substrate HyperNEAT (ES-HyperNEAT) produce networks that stagnate at random-guessing performance, wasting computational resources. We frame early stopping as binary classification on early fitness trajectories: for each trial, we compute the cumulative median of best-per-generation fitness and test it against a threshold derived by maximizing the F1 score on an initial 90-trial dataset. The resulting rule (generation G* = 3, threshold T* = 0.140) achieves F1 = 0.872 on 180 independent validation trials, retaining over 90% of successful trials while cutting computational cost by 41.6%. Compared to Hyperband, our domain-specific rule is 64% more efficient with higher mean fitness, though Hyperband occasionally discovers higher peak solutions. On a converged search population the rule becomes too aggressive (recall 31.1%), motivating adaptive thresholds. The specific thresholds are ES-HyperNEAT-specific, but the methodology, deriving stopping criteria from fitness dynamics classification, is applicable to other evolutionary algorithms with stagnation-prone hyperparameter spaces.

cs.NE↗

arXiv2026

Breaking the Central Bias: Spatially Partitioned Experts for Coordinate-Based Neuroevolution

Evolvable-Substrate HyperNEAT (ES-HyperNEAT), a bio-inspired indirect encoding that determines neuron placement and connection weights from spatial coordinates, exhibits a failure mode on MNIST as a diagnostic benchmark. Because input pixels map to a coordinate space centered at the origin, evolved networks converge on a small central cluster of input pixels, a spatial-concentration bias; prior work observed only 21% mean accuracy in this regime. Is this bias an optimization artifact or an architectural ceiling? Inspired by Mixture-of-Experts (MoE) principles, we partition the input into non-overlapping spatial segments, each assigned to a separately evolved specialist network. With 13 such experts, this design reaches 43% mean accuracy, a 106% relative improvement over the baseline. The architectural gain does not depend on data-driven aggregation: equal-weighted averaging, which uses no validation data, already yields a 70% improvement; the gain comes from partitioning, not the weighting. Receptive-field analysis shows the mechanism: partitioning forces evolution to discover features across the entire image, expanding active pixel coverage from 4% to 79%. Absolute accuracy stays below gradient-trained baselines, but the relative gain points to central bias, not the evolutionary search. Two tools are designed to generalize beyond MNIST: a receptive-field diagnostic for silent input-coverage collapse, and a spatial-partitioning remedy that restores coverage.

cs.NE↗

arXiv2026

DRAMatic Speedup: Accelerating HE Operations on a Processing-in-Memory System

Homomorphic encryption (HE) is a promising technology for confidential cloud computing, as it allows computations on encrypted data. However, HE is computationally expensive and often memory-bound on conventional computer architectures. Processing-in-Memory (PIM) is an alternative hardware architecture that integrates processing units and memory on the same chip or memory module. PIM enables higher memory bandwidth than conventional architectures and could thus be suitable for accelerating HE. We present DRAMatic, which implements operations foundational to HE on UPMEM PIM -- a programmable general-purpose PIM system developed by UPMEM. DRAMatic incorporates many arithmetic optimizations, including residue number system and number-theoretic transform techniques, and can support the large parameters required for secure homomorphic evaluations. It achieves a 334 times speed-up compared to previous HE implementations on UPMEM PIM. We also evaluate DRAMatic against Microsoft SEAL, a popular open-source HE library, regarding both runtime and energy efficiency. The results show that DRAMatic significantly closes the gap between Microsoft SEAL and HE implementations on UPMEM PIM. However, we also show that DRAMatic is currently constrained by data transfer overhead and limited multiplication performance on UPMEM PIM hardware. Finally, we discuss potential hardware extensions to UPMEM PIM.

cs.CR↗

arXiv2026

PIM-CACHE: High-Efficiency Content-Aware Copy for Processing-In-Memory

Processing-in-memory (PIM) architectures bring computation closer to data, reducing the processor-memory transfer bottleneck in traditional processor-centric designs. Novel hardware solutions, such as UPMEM's in-memory processing technology, achieve this by integrating low-power DRAM processing units (DPUs) into memory DIMMs, enabling massive parallelism and improved memory bandwidth. However, paradoxically, these PIM architectures introduce mandatory coarse-grained data transfers between host DRAM and DPUs, which often become the new bottleneck. We present PIM-CACHE, a lightweight data staging layer that dynamically eliminates redundant data transfers to PIM DPUs by exploiting workload similarity, achieving content-aware copy (CAC). We evaluate PIM-CACHE on both synthetic workloads and real-world genome datasets, demonstrating its effectiveness in reducing PIM data transfer overhead.

cs.ET↗

arXiv2025

TriHaRd: Higher Resilience for TEE Trusted Time

Accurately measuring time passing is critical for many applications. However, in Trusted Execution Environments (TEEs) such as Intel SGX, the time source is outside the Trusted Computing Base: a malicious host can manipulate the TEE's notion of time, jumping in time or affecting perceived time speed. Previous work (Triad) proposes protocols for TEEs to maintain a trustworthy time source by building a cluster of TEEs that collaborate with each other and with a remote Time Authority to maintain a continuous notion of passing time. However, such approaches still allow an attacker to control the operating system and arbitrarily manipulate their own TEE's perceived clock speed. An attacker can even propagate faster passage of time to honest machines participating in Triad's trusted time protocol, causing them to skip to timestamps arbitrarily far in the future. We propose TriHaRd, a TEE trusted time protocol achieving high resilience against clock speed and offset manipulations, notably through Byzantine-resilient clock updates and consistency checks. We empirically show that TriHaRd mitigates known attacks against Triad.

cs.CR↗

arXiv2025

IM-PIR: In-Memory Private Information Retrieval

Private information retrieval (PIR) is a cryptographic primitive that allows a client to securely query one or multiple servers without revealing their specific interests. In spite of their strong security guarantees, current PIR constructions are computationally costly. Specifically, most PIR implementations are memory-bound due to the need to scan extensive databases (in the order of GB), making them inherently constrained by the limited memory bandwidth in traditional processor-centric computing architectures. Processing-in-memory (PIM) is an emerging computing paradigm that augments memory with compute capabilities, addressing the memory bandwidth bottleneck while simultaneously providing extensive parallelism. Recent research has demonstrated PIM's potential to significantly improve performance across a range of data-intensive workloads, including graph processing, genome analysis, and machine learning. In this work, we propose the first PIM-based architecture for multi-server PIR. We discuss the algorithmic foundations of the latter and show how its operations align with the core strengths of PIM architectures: extensive parallelism and high memory bandwidth. Based on this observation, we design and implement IM-PIR, a PIM-based multi-server PIR approach on top of UPMEM PIM, the first openly commercialized PIM architecture. Our evaluation demonstrates that a PIM-based multi-server PIR implementation significantly improves query throughput by more than 3.7x when compared to a standard CPU-based PIR approach.

cs.DC↗

arXiv2025

Montsalvat: Partitioning Java Applications to Minimize the TCB in Intel SGX

The popularity of the Java programming language has led to its wide adoption in cloud computing infrastructures. However, Java applications running in untrusted clouds are vulnerable to various forms of privileged attacks. The emergence of trusted execution environments (TEEs) such as Intel SGX mitigates this problem. TEEs protect code and data in secure enclaves inaccessible to untrusted software, including the kernel and hypervisors. To efficiently use TEEs, developers must manually partition their applications into trusted and untrusted parts, in order to reduce the size of the trusted computing base (TCB) and minimise the risks of security vulnerabilities. However, partitioning applications poses two important challenges: (i) ensuring efficient object communication between the partitioned components, and (ii) ensuring the consistency of garbage collection between the parts, especially with memory-managed languages such as Java. We present Montsalvat, a tool which provides a practical and intuitive annotation-based partitioning approach for Java applications destined for secure enclaves. Montsalvat provides an RMI-like mechanism to ensure inter-object communication, as well as consistent garbage collection across the partitioned components. We implement Montsalvat with GraalVM native-image, a tool for compiling Java applications ahead-of-time into standalone native executables that do not require a JVM at runtime. Our extensive evaluation with micro- and macro-benchmarks shows our partitioning approach to boost performance in real-world applications

cs.CR↗

arXiv2025

Plinius: Secure and Persistent Machine Learning Model Training

With the increasing popularity of cloud based machine learning (ML) techniques there comes a need for privacy and integrity guarantees for ML data. In addition, the significant scalability challenges faced by DRAM coupled with the high access-times of secondary storage represent a huge performance bottleneck for ML systems. While solutions exist to tackle the security aspect, performance remains an issue. Persistent memory (PM) is resilient to power loss (unlike DRAM), provides fast and fine-granular access to memory (unlike disk storage) and has latency and bandwidth close to DRAM (in the order of ns and GB/s, respectively). We present PLINIUS, a ML framework using Intel SGX enclaves for secure training of ML models and PM for fault tolerance guarantees. PLINIUS uses a novel mirroring mechanism to create and maintain (i) encrypted mirror copies of ML models on PM, and (ii) encrypted training data in byte-addressable PM, for near-instantaneous data recovery after a system failure. Compared to disk-based checkpointing systems, PLINIUS is 3.2x and 3.7x faster respectively for saving and restoring models on real PM hardware, achieving robust and secure ML model training in SGX enclaves.

cs.CR↗

arXiv2025

ScamDetect: Towards a Robust, Agnostic Framework to Uncover Threats in Smart Contracts

Smart contracts have transformed decentralized finance by enabling programmable, trustless transactions. However, their widespread adoption and growing financial significance have attracted persistent and sophisticated threats, such as phishing campaigns and contract-level exploits. Traditional transaction-based threat detection methods often expose sensitive user data and interactions, raising privacy and security concerns. In response, static bytecode analysis has emerged as a proactive mitigation strategy, identifying malicious contracts before they execute harmful actions. Building on this approach, we introduced PhishingHook, the first machine-learning-based framework for detecting phishing activities in smart contracts via static bytecode and opcode analysis, achieving approximately 90% detection accuracy. Nevertheless, two pressing challenges remain: (1) the increasing use of sophisticated bytecode obfuscation techniques designed to evade static analysis, and (2) the heterogeneity of blockchain environments requiring platform-agnostic solutions. This paper presents a vision for ScamDetect (Smart Contract Agnostic Malware Detector), a robust, modular, and platform-agnostic framework for smart contract malware detection. Over the next 2.5 years, ScamDetect will evolve in two stages: first, by tackling obfuscated Ethereum Virtual Machine (EVM) bytecode through graph neural network (GNN) analysis of control flow graphs (CFGs), leveraging GNNs' ability to capture complex structural patterns beyond opcode sequences; and second, by generalizing detection capabilities to emerging runtimes such as WASM. ScamDetect aims to enable proactive, scalable security for the future of decentralized ecosystems.

cs.CR↗

arXiv2025

PhishingHook: Catching Phishing Ethereum Smart Contracts leveraging EVM Opcodes

The Ethereum Virtual Machine (EVM) is a decentralized computing engine. It enables the Ethereum blockchain to execute smart contracts and decentralized applications (dApps). The increasing adoption of Ethereum sparked the rise of phishing activities. Phishing attacks often target users through deceptive means, e.g., fake websites, wallet scams, or malicious smart contracts, aiming to steal sensitive information or funds. A timely detection of phishing activities in the EVM is therefore crucial to preserve the user trust and network integrity. Some state-of-the art approaches to phishing detection in smart contracts rely on the online analysis of transactions and their traces. However, replaying transactions often exposes sensitive user data and interactions, with several security concerns. In this work, we present PhishingHook, a framework that applies machine learning techniques to detect phishing activities in smart contracts by directly analyzing the contract's bytecode and its constituent opcodes. We evaluate the efficacy of such techniques in identifying malicious patterns, suspicious function calls, or anomalous behaviors within the contract's code itself before it is deployed or interacted with. We experimentally compare 16 techniques, belonging to four main categories (Histogram Similarity Classifiers, Vision Models, Language Models and Vulnerability Detection Models), using 7,000 real-world malware smart contracts. Our results demonstrate the efficiency of PhishingHook in performing phishing classification systems, with about 90% average accuracy among all the models. We support experimental reproducibility, and we release our code and datasets to the research community.

cs.CR↗

arXiv2025

Practical Secure Aggregation by Combining Cryptography and Trusted Execution Environments

Secure aggregation enables a group of mutually distrustful parties, each holding private inputs, to collaboratively compute an aggregate value while preserving the privacy of their individual inputs. However, a major challenge in adopting secure aggregation approaches for practical applications is the significant computational overhead of the underlying cryptographic protocols, e.g. fully homomorphic encryption. This overhead makes secure aggregation protocols impractical, especially for large datasets. In contrast, hardware-based security techniques such as trusted execution environments (TEEs) enable computation at near-native speeds, making them a promising alternative for reducing the computational burden typically associated with purely cryptographic techniques. Yet, in many scenarios, parties may opt for either cryptographic or hardware-based security mechanisms, highlighting the need for hybrid approaches. In this work, we introduce several secure aggregation architectures that integrate both cryptographic and TEE-based techniques, analyzing the trade-offs between security and performance.

cs.CR↗

arXiv2024

Evaluating the Potential of In-Memory Processing to Accelerate Homomorphic Encryption

The widespread adoption of cloud-based solutions introduces privacy and security concerns. Techniques such as homomorphic encryption (HE) mitigate this problem by allowing computation over encrypted data without the need for decryption. However, the high computational and memory overhead associated with the underlying cryptographic operations has hindered the practicality of HE-based solutions. While a significant amount of research has focused on reducing computational overhead by utilizing hardware accelerators like GPUs and FPGAs, there has been relatively little emphasis on addressing HE memory overhead. Processing in-memory (PIM) presents a promising solution to this problem by bringing computation closer to data, thereby reducing the overhead resulting from processor-memory data movements. In this work, we evaluate the potential of a PIM architecture from UPMEM for accelerating HE operations. Firstly, we focus on PIM-based acceleration for polynomial operations, which underpin HE algorithms. Subsequently, we conduct a case study analysis by integrating PIM into two popular and open-source HE libraries, OpenFHE and HElib. Our study concludes with key findings and takeaways gained from the practical application of HE operations using PIM, providing valuable insights for those interested in adopting this technology.

cs.CR↗

arXiv2024

On the Cost of Model-Serving Frameworks: An Experimental Evaluation

In machine learning (ML), the inference phase is the process of applying pre-trained models to new, unseen data with the objective of making predictions. During the inference phase, end-users interact with ML services to gain insights, recommendations, or actions based on the input data. For this reason, serving strategies are nowadays crucial for deploying and managing models in production environments effectively. These strategies ensure that models are available, scalable, reliable, and performant for real-world applications, such as time series forecasting, image classification, natural language processing, and so on. In this paper, we evaluate the performances of five widely-used model serving frameworks (TensorFlow Serving, TorchServe, MLServer, MLflow, and BentoML) under four different scenarios (malware detection, cryptocoin prices forecasting, image classification, and sentiment analysis). We demonstrate that TensorFlow Serving is able to outperform all the other frameworks in serving deep learning (DL) models. Moreover, we show that DL-specific frameworks (TensorFlow Serving and TorchServe) display significantly lower latencies than the three general-purpose ML frameworks (BentoML, MLFlow, and MLServer).

cs.LG↗

arXiv2024

BlindexTEE: A Blind Index Approach towards TEE-supported End-to-end Encrypted DBMS

Using cloud-based applications comes with privacy implications, as the end-user looses control over their data. While encrypting all data on the client is possible, it largely reduces the usefulness of database management systems (DBMS) that are typically built to efficiently query large quantities of data. We present BlindexTEE, a new component that sits between the application business-logic and the database. BlindexTEE is shielded from malicious users or compromised environments by executing inside an SEV-SNP confidential VM, AMD's trusted execution environment (TEE). BlindexTEE is in charge of end-to-end encryption of user data while preserving the ability of the DBMS to efficiently filter data. By decrypting and re-encrypting data, it builds blind indices, used later on to efficiently query the DBMS. We demonstrate the practicality of BlindexTEE with MySQL in several micro- and macro-benchmarks, achieving overheads between 36.1% and 462% over direct database access depending on the usage scenario.

cs.CR↗

arXiv2024

CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques

This paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices, the ability to efficiently model and forecast the time series resulting from the exchange price volatility remains an open research challenge. Good results can been achieved with state-of-the-art ML techniques, including Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs). CryptoAnalytics is a software toolkit to easily train these models and make inference on up-to-date cryptocoin trading price data, with facilities to fetch datasets from one of the main leading aggregator websites, i.e., CoinMarketCap, train models and infer the future trends. This software is implemented in Python. It relies on PyTorch for the implementation of RNNs (LSTM and GRU), while for GBMs, it leverages on XgBoost, LightGBM and CatBoost.

cs.CE↗

arXiv2024

Practical Forecasting of Cryptocoins Timeseries using Correlation Patterns

Cryptocoins (i.e., Bitcoin, Ether, Litecoin) are tradable digital assets. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins among owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. The extreme volatility of such trading prices across all different sets of crypto-assets remains undisputed. However, the relations between the trading prices across different cryptocoins remains largely unexplored. Major coin exchanges indicate trend correlation to advise for sells or buys. However, price correlations remain largely unexplored. We shed some light on the trend correlations across a large variety of cryptocoins, by investigating their coin/price correlation trends over the past two years. We study the causality between the trends, and exploit the derived correlations to understand the accuracy of state-of-the-art forecasting techniques for time series modeling (e.g., GBMs, LSTM and GRU) of correlated cryptocoins. Our evaluation shows (i) strong correlation patterns between the most traded coins (e.g., Bitcoin and Ether) and other types of cryptocurrencies, and (ii) state-of-the-art time series forecasting algorithms can be used to forecast cryptocoins price trends. We released datasets and code to reproduce our analysis to the research community.

cs.CE↗