  The rise of IT-dependent operations in modern organizations has heightened
their vulnerability to cyberattacks. As a growing number of organizations
include smart, interconnected devices in their systems to automate their
processes, the attack surface becomes much bigger, and the complexity and
frequency of attacks pose a significant threat. Consequently, organizations
have been compelled to seek innovative approaches to mitigate the menaces
inherent in their infrastructure. In response, considerable research efforts
have been directed towards creating effective solutions for sharing Cyber
Threat Intelligence (CTI). Current information-sharing methods lack privacy
safeguards, leaving organizations vulnerable to leaks of both proprietary and
confidential data. To tackle this problem, we designed a novel framework called
SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm Learning
and Blockchain technologies to enable businesses to collaborate, preserving the
privacy of their CTI data. Moreover, our approach provides a way to assess the
data and model quality, and the trustworthiness of all the participants
leveraging some validators through Zero Knowledge Proofs. An extensive
experimental campaign demonstrates our framework's correctness and performance,
and the detailed attack model discusses its robustness against attacks in the
context of data and model quality.
