most cited"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SE2026

Tiny Machine-Learning Operations within Cyber-Physical Systems: a Field Study

Filippo Scaramuzza, Damian A. Tamburri

Machine-Learning Operations (MLOps) is maturing into a software-engineering discipline, yet its tiny-scale variant (TinyMLOps)-targeting the resource-constrained microcontrollers e…

cs.ET2026

Data Product MCP: Chat with your Enterprise Data

Marco Tonnarelli, Filippo Scaramuzza, Simon Harrer +1

Computational data governance aims to make the enforcement of governance policies and legal obligations more efficient and reliable. Recent advances in natural language processing…

cs.SE20261 cited

"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems

Filippo Scaramuzza, Renato Cordeiro Ferreira, Giovanni Quattrocchi +2

Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the traditional mechanisms used to achieve the ver…

cs.SE2025

Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap

Filippo Scaramuzza, Damian A. Tamburri, Willem-Jan van den Heuvel

This vision paper presents initial research on assessing the robustness and reliability of AI-enabled systems, and key factors in ensuring their safety and effectiveness in practic…

cs.SE2025

Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs

Filippo Scaramuzza, Giovanni Quattrocchi, Damian A. Tamburri

As Artificial Intelligence (AI) systems, particularly those based on machine learning (ML), become integral to high-stakes applications, their probabilistic and opaque nature poses…