Privacy-preserving Blockchain-enabled Parametric Insurance via Remote Sensing and IoT

• Main Authors: Hao, Mingyu, Qian, Keyang, Chau, Sid Chi-Kin
• Format: Journal Article
• Language: English
• Published: 15-05-2023
• Subjects: Computer Science - Cryptography and Security; Computer Science - Networking and Internet Architecture
• DOI: 10.48550/arxiv.2305.08384

Traditional Insurance, a popular approach of financial risk management, has
suffered from the issues of high operational costs, opaqueness, inefficiency
and a lack of trust. Recently, blockchain-enabled "parametric insurance"
through authorized data sources (e.g., remote sensing and IoT) aims to overcome
these issues by automating the underwriting and claim processes of insurance
policies on a blockchain. However, the openness of blockchain platforms raises
a concern of user privacy, as the private user data in insurance claims on a
blockchain may be exposed to outsiders. In this paper, we propose a
privacy-preserving parametric insurance framework based on succinct
zero-knowledge proofs (zk-SNARKs), whereby an insuree submits a zero-knowledge
proof (without revealing any private data) for the validity of an insurance
claim and the authenticity of its data sources to a blockchain for transparent
verification. Moreover, we extend the recent zk-SNARKs to support robust
privacy protection for multiple heterogeneous data sources and improve its
efficiency to cut the incurred gas cost by 80%. As a proof-of-concept, we
implemented a working prototype of bushfire parametric insurance on real-world
blockchain platform Ethereum, and present extensive empirical evaluations.