PINSA: Privacy-preserving cyber insurance framework
Authors/Creators
Description
Cybercriminals continually advance their tactics, exploit novel attack vectors, and target emerging technologies. To mitigate these risks, cyber insurance policies must remain abreast of the latest technological developments. Staying technologically updated enables an Insurance Company (IC) to assess risks more precisely, tailor policies to a potential potential Policyholder (PH), and accurately calculate fair premiums. By incorporating innovative risk assessment methodologies, robust Know-Your-Customer protocols, and automated claims-handling processes, ICs can offer tailored and cost-effective solutions to their PHs. Embracing technological advancements enables the field of cyber insurance to adapt to the ever-changing landscape of cyber threats, providing comprehensive protection to organizations. This article introduces PINSA, an innovative privacy-preserving framework designed to deliver robust security and privacy assurances to PHs against honest but inquisitive entities within the cyber insurance ecosystem. PINSA also equips ICs with automated processes for claims management. At the core of PINSA lies Hyperledger Aries, leveraging verifiable credentials to empower PHs with identity ownership and data control. Our framework is complemented by Hyperledger Fabric, which imbues PINSA with intelligent functionalities enabling PHs and ICs to autonomously execute actions related to cyber insurance and gather historical cybersecurity data. In this direction, we have successfully implemented the key components of PINSA and conducted a quantitative performance assessment. We also substantiate its security and privacy attributes, confirming that PINSA effectively achieves its objectives. In summary, PINSA represents a forward-thinking solution poised to enhance cyber insurance in an era of ever-evolving cyber threats, offering a promising avenue for safeguarding organizations and policyholders in the digital landscape.
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s10586-026-05996-z.pdf
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(4.4 MB)
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