Privacy-preserving analytics improved with identity verification methods
On the Usage of Verifiable Credentials in Privacy-Preserving Federated Analytics
Cryptography and SecurityEmerging Technologies
Summary
Sometimes, many computers want to work together to learn things from their combined data without sharing the actual data. This can be tricky when you want to make sure who is sending data and that the data is correct, without risking privacy or leaks. The authors looked at whether using special digital ID cards, called Verifiable Credentials, can help prove identities and keep data intact during this process. They connect these ID cards with a method called Federated Privacy-Preserving Analytics to see if it all works safely and reliably.
What this means in practice
- •For distributed computing teams: Enable secure identity verification for collaborators in privacy-focused distributed data analysis workflows.
- •For healthcare data managers: Support privacy-respecting cooperation between hospitals sharing analytics without exposing patient data identities.
A position paper. It proposes an approach and reports no results.
Authors
Andreea-Elena Drăgnoiu, Ruxandra F. Olimid
Abstract
Privacy-Preserving Federated Analytics enables multiple nodes to collaboratively derive statistical insights without exchanging raw data. However, ensuring node authenticity and data validation (avoiding concerns such as identity tracking, data linkage, or data leakage) remain fundamental challenges. This paper offers some insights into the feasibility of defining an authenticated, integrity-preserving data model by coupling FPPA with Verifiable Credentials defined within the Self-Sovereign Identity framework.