PETs Use Case: Differential Privacy for End-of-Life Data

In this use case, Oblivious partnered with an insurance company to tackle a common tension between data privacy and utility: how to retain meaningful insights from personal data while complying with legal requirements to delete it. By applying Differential Privacy, the organization can preserve actuarial insights without violating global privacy laws, generating differentially private statistical “snapshots” before erasing the underlying microdata. This approach creates a privacy-preserving middle ground between wholesale deletion (which eliminates analytical value) and indefinite retention (which risks noncompliance and harm). The system automates this transformation under strict governance controls, integrates with the company’s cloud environment, and deletes the source data once processed. Differential Privacy’s mathematical guarantees ensure that no individual can be reidentified, enabling organizations to model risk, refine premiums, and maintain business continuity without compromising Privacy.

The Research Coordination Network (RCN) for Privacy-Preserving Data Sharing and Analytics is supported by the U.S. National Science Foundation under Award #2413978 and the U.S. Department of Energy, Office of Science under Award #DE-SC0024884.