arXiv subjects

Salvatore D'Antonio

Publications and source records attributed to Salvatore D'Antonio.

5 recordsLinked to original sources

arXiv2025

Enhancing the Security of Rollup Sequencers using Decentrally Attested TEEs

The growing scalability demand of public Blockchains led to the rise of Layer-2 solutions, such as Rollups. Rollups improve transaction throughput by processing operations off-chain and posting the results on-chain. A critical component in Rollups is the Sequencer, responsible for receiving, ordering and batching transactions before they are submitted to the Layer-1 blockchain. While essential, the centralized nature of the Sequencer makes it vulnerable to attacks, such as censorship, transaction manipulation and tampering. To enhance its security, there are solutions in the literature that shield the Sequencer inside a Trusted Execution Environment (TEE). However, the attestation of TEEs introduces additional centralization, which is in contrast with the core Blockchain principle. In this paper, we propose a TEE-secured Sequencer equipped with a decentralized attestation mechanism. We outline the design and implementation of our solution, covering the system architecture, TEE integration, and the decentralization of the attestation process. Additionally, we present an experimental evaluation conducted on a realistic Rollup testnet. Our results show that this approach strengthens Sequencer integrity without sacrificing compatibility or deployability in existing Layer-2 architectures.

cs.CR↗

arXiv2024

An Experimental Evaluation of TEE technology Evolution: Benchmarking Transparent Approaches based on SGX, SEV, and TDX

Protection of data-in-use is a key priority, for which Trusted Execution Environment (TEE) technology has unarguably emerged as a, possibly the most, promising solution. Multiple server-side TEE offerings have been released over the years, exhibiting substantial differences with respect to several aspects. The first comer was Intel SGX, which featured Process-based TEE protection, an efficient yet difficult to use approach. Some SGX limitations were (partially) overcome by runtimes, notably: Gramine, Scone, and Occlum. A major paradigm shift was later brought by AMD SEV, with VM-based TEE protection, which enabled lift-and-shift deployment of legacy applications. This new paradigm has been implemented by Intel only recently, in TDX. While the threat model of the aforementioned TEE solutions has been widely discussed, a thorough performance comparison is still lacking in the literature. This paper provides a comparative evaluation of TDX, SEV, Gramine-SGX, and Occlum-SGX. We study computational overhead and resource usage, under different operational scenarios and using a diverse suite of legacy applications. By doing so, we provide a reliable performance assessment under realistic conditions. We explicitly emphasize that, at the time of writing, TDX was not yet available to the public. Thus, the evaluation of TDX is a unique feature of this study.

cs.CR↗

arXiv2021

Privacy-preserving Credit Scoring via Functional Encryption

The majority of financial organizations managing confidential data are aware of security threats and leverage widely accepted solutions (e.g., storage encryption, transport-level encryption, intrusion detection systems) to prevent or detect attacks. Yet these hardening measures do little to face even worse threats posed on data-in-use. Solutions such as Homomorphic Encryption (HE) and hardware-assisted Trusted Execution Environment (TEE) are nowadays among the preferred approaches for mitigating this type of threat. However, given the high-performance overhead of HE, financial institutions -- whose processing rate requirements are stringent -- are more oriented towards TEE-based solutions. The X-Margin Inc. company, for example, offers secure financial computations by combining the Intel SGX TEE technology and HE-based Zero-Knowledge Proofs, which shield customers' data-in-use even against malicious insiders, i.e., users having privileged access to the system. Despite such a solution offers strong security guarantees, it is constrained by having to trust Intel and by the SGX hardware extension availability. In this paper, we evaluate a new frontier for X-Margin, i.e., performing privacy-preserving credit risk scoring via an emerging cryptographic scheme: Functional Encryption (FE), which allows a user to only learn a function of the encrypted data. We describe how the X-Margin application can benefit from this innovative approach and -- most importantly -- evaluate its performance impact.

cs.CR↗

arXiv2014

Big Data Analytics for QoS Prediction Through Probabilistic Model Checking

As competitiveness increases, being able to guaranting QoS of delivered services is key for business success. It is thus of paramount importance the ability to continuously monitor the workflow providing a service and to timely recognize breaches in the agreed QoS level. The ideal condition would be the possibility to anticipate, thus predict, a breach and operate to avoid it, or at least to mitigate its effects. In this paper we propose a model checking based approach to predict QoS of a formally described process. The continous model checking is enabled by the usage of a parametrized model of the monitored system, where the actual value of parameters is continuously evaluated and updated by means of big data tools. The paper also describes a prototype implementation of the approach and shows its usage in a case study.

cs.SE↗

arXiv2014

Monitoring service quality: methods and solutions to implement a managerial dash-board to improve software development

The software used for running and handling the inter-bank network framework provides services with extremely strict uptime (above 99.98 percent) and quality requirements, thus tools to trace and manage changes as well as metrics to measure process quality are essential. Having conducted a two year long campaign of data collection and activity monitoring it has been possible to analyze a huge amount of process data from which many aggregated indicators were derived, selected and evaluated for providing a managerial dash-board to monitor software development.

cs.SE↗