Optimally Designing Cybersecurity Insurance Contracts to Encourage the Sharing of Medical Data
Yoon Lee, Anil Aswani

TL;DR
This paper designs optimal cybersecurity insurance contracts to incentivize health care providers to share medical data securely, addressing privacy and liability risks through a principal-agent model with moral hazard.
Contribution
It introduces a novel framework for designing insurance contracts that promote responsible medical data sharing, considering AI re-identification risks and provider incentives.
Findings
Derived optimal insurance contracts for different sharing scenarios
Analyzed implications of insurance on data sharing behavior
Performed numerical case studies demonstrating contract effectiveness
Abstract
Though the sharing of medical data has the potential to lead to breakthroughs in health care, the sharing process itself exposes patients and health care providers to various risks. Patients face risks due to the possible loss in privacy or livelihood that can occur when medical data is stolen or used in non-permitted ways, whereas health care providers face risks due to the associated liability. For medical data, these risks persist even after anonymizing/deidentifying, according to the standards defined in existing legislation, the data sets prior to sharing, because shared medical data can often be deanonymized/reidentified using advanced artificial intelligence and machine learning methodologies. As a result, health care providers are hesitant to share medical data. One possible solution to encourage health care providers to responsibly share data is through the use of cybersecurity…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBlockchain Technology Applications and Security · Privacy-Preserving Technologies in Data
