Luigi Romano

Giovanni Maria Cristiano, Salvatore D'Antonio, Jonah Giglio et al.

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.

Nicola Bena, Claudia Diamantini, Michela Natilli et al.

The proceedings of Workshop Scientific HPC in the pre-Exascale era (SHPC), held in Pisa, Italy, September 18, 2024, are part of 3rd Italian Conference on Big Data and Data Science (ITADATA2024) proceedings (arXiv: 2503.14937). The main objective of SHPC workshop was to discuss how the current most critical questions in HPC emerge in astrophysics, cosmology, and other scientific contexts and experiments. In particular, SHPC workshop focused on: $\bullet$ Scientific (mainly in astrophysical and medical fields) applications toward (pre-)Exascale computing $\bullet$ Performance portability $\bullet$ Green computing $\bullet$ Machine learning $\bullet$ Big Data management $\bullet$ Programming on heterogeneous architectures $\bullet$ Programming on accelerators $\bullet$ I/O techniques

Nicola Bena, Claudia Diamantini, Michela Natilli et al.

Proceedings of the 3rd Italian Conference on Big Data and Data Science (ITADATA2024), held in Pisa, Italy, September 17-19, 2024. The Italian Conference on Big Data and Data Science (ITADATA2024) is the annual event supported by the CINI Big Data National Laboratory and ISTI CNR that aims to put together Italian researchers and professionals from academia, industry, government, and public administration working in the field of big data and data science, as well as related fields (e.g., security and privacy, HPC, Cloud). ITADATA2024 covered research on all theoretical and practical aspects of Big Data and data science including data governance, data processing, data analysis, data reporting, data protection, as well as experimental studies and lessons learned. In particular, ITADATA2024 focused on - Data spaces - Data processing life cycle - Machine learning and Large Language Models - Applications of big data and data science in healthcare, finance, industry 5.0, and beyond - Data science for social network analysis

Giuseppe Cicotti, Luigi Coppolino, Salvatore D'Antonio et al.

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.