arXiv subjects

Francesco Fabris

Publications and source records attributed to Francesco Fabris.

6 recordsLinked to original sources

arXiv2025

Formal Modeling and Verification of the Algorand Consensus Protocol in CADP

Algorand is a scalable and secure permissionless blockchain that achieves proof-of-stake consensus via cryptographic self-sortition and binary Byzantine agreement. In this paper we present a process algebraic model of the Algorand consensus protocol with the aim of enabling formal verification. Our model captures the behavior of participants in terms of the structured alternation of consensus steps toward a committee-based agreement. We validate the correctness of the protocol in the absence of adversaries and then extend our model to assess the influence of coordinated malicious nodes that can force the commit of an empty block instead of the proposed one. The adversarial scenario is analyzed through an equivalence-checking-based noninterference framework that we have implemented in the CADP verification toolkit. In addition to highlighting both the robustness and the limitations of the Algorand protocol under adversarial assumptions, this work illustrates the added value of using formal methods for the analysis of consensus algorithms within blockchains.

cs.DC↗

arXiv2025

Redactable Blockchains: An Overview

Blockchains are widely recognized for their immutability, which provides robust guarantees of data integrity and transparency. However, this same feature poses significant challenges in real-world situations that require regulatory compliance, correction of erroneous data, or removal of sensitive information. Redactable blockchains address the limitations of traditional ones by enabling controlled, auditable modifications to blockchain data, primarily through cryptographic mechanisms such as chameleon hash functions and alternative redaction schemes. This report examines the motivations for introducing redactability, surveys the cryptographic primitives that enable secure edits, and analyzes competing approaches and their shortcomings. Special attention is paid to the practical deployment of redactable blockchains in private settings, with discussions of use cases in healthcare, finance, Internet of drones, and federated learning. Finally, the report outlines further challenges, also in connection with reversible computing, and the future potential of redactable blockchains in building law-compliant, trustworthy, and scalable digital infrastructures.

cs.CR↗

arXiv2025

On the Operational Resilience of CBDC: Threats and Prospects of Formal Validation for Offline Payments

Information and communication technologies are by now employed in most human activities, including economics and finance. Modern computers have reached an extraordinary power in terms of information processing, storage, retrieval, and transmission. However, several results of theoretical computer science imply the impossibility of certifying software quality in general. With the exception of safety-critical systems, this has primarily concerned information processed by confined systems, with limited socio-economic consequences. In the emerging era of technologies for exchanging tokenized assets and digital money over the Internet, such as in particular central bank digital currency (CBDC), even a minor bug could trigger a financial collapse. Although the aforementioned impossibility results cannot be overcome in an absolute sense, there exist formal methods that can provide correctness assertions for software system models under suitable conditions. We advocate their use to validate the operational resilience of software infrastructures for CBDC by framing the offline payment problem, organizing its threat landscape, and outlining a formal methods methodology, illustrated by a minimal proof of concept, that we argue should underpin CBDC design and deployment.

cs.DC↗

arXiv2025

Significativity Indices for Agreement Values

Agreement measures, such as Cohen's kappa or intraclass correlation, gauge the matching between two or more classifiers. They are used in a wide range of contexts from medicine, where they evaluate the effectiveness of medical treatments and clinical trials, to artificial intelligence, where they can quantify the approximation due to the reduction of a classifier. The consistency of different classifiers to a golden standard can be compared simply by using the order induced by their agreement measure with respect to the golden standard itself. Nevertheless, labelling an approach as good or bad exclusively by using the value of an agreement measure requires a scale or a significativity index. Some quality scales have been proposed in the literature for Cohen's kappa, but they are mainly na\"ive, and their boundaries are arbitrary. This work proposes a general approach to evaluate the significativity of any agreement value between two classifiers and introduces two significativity indices: one dealing with finite data sets, the other one handling classification probability distributions. Moreover, this manuscript addresses the computational challenges of evaluating such indices and proposes some efficient algorithms for their evaluation.

cs.LG↗

arXiv2020

Computing Information Agreement

Agreement measures are useful to both compare different evaluations of the same diagnostic outcomes and validate new rating systems or devices. Information Agreement (IA) is an information-theoretic-based agreement measure introduced to overcome all the limitations and alleged pitfalls of Cohen's Kappa. However, it is only able to deal with agreement matrices whose values are positive natural numbers. This work extends IA admitting also 0 as a possible value for the agreement matrix cells.

cs.IT↗

arXiv2015

PolyMorph: Increasing P300 Spelling Efficiency by Selection Matrix Polymorphism and Sentence-Based Predictions

P300 is an electric signal emitted by brain about 300 milliseconds after a rare, but relevant-for-the-user event. One of the applications of this signal is sentence spelling that enables subjects who lost the control of their motor pathways to communicate by selecting characters in a matrix containing all the alphabet symbols. Although this technology has made considerable progress in the last years, it still suffers from both low communication rate and high error rate. This article presents a P300 speller, named PolyMorph, that introduces two major novelties in the field: the selection matrix polymorphism, that reduces the size of the selection matrix itself by removing useless symbols, and sentence-based predictions, that exploit all the spelt characters of a sentence to determine the probability of a word. In order to measure the effectiveness of the presented speller, we describe two sets of tests: the first one in vivo and the second one in silico. The results of these experiments suggest that the use of PolyMorph in place of the naive character-by-character speller both increases the number of spelt characters per time unit and reduces the error rate.

cs.HC↗