# Open Algorithms for Identity Federation

## TL;DR

This paper introduces the OPAL paradigm, enabling privacy-preserving, algorithm-based data sharing in identity federation to improve data utility while maintaining privacy and fairness.

## Contribution

It proposes a novel approach to identity data sharing using open algorithms governed by trust networks, moving beyond static attribute exchange.

## Key findings

- Enables privacy-preserving data sharing through vetted algorithms
- Improves data utility for service providers
- Supports fair and unbiased data sharing

## Abstract

The identity problem today is a data-sharing problem. Today the fixed attributes approach adopted by the consumer identity management industry provides only limited information about an individual, and therefore is of limited value to the service providers and other participants in the identity ecosystem. This paper proposes the use of the Open Algorithms (OPAL) paradigm to address the increasing need for individuals and organizations to share data in a privacy-preserving manner. Instead of exchanging static or fixed attributes about users, participants in the ecosystem will be able to obtain better insight through a collective sharing of algorithms, governed through a trust network. Algorithms for specific data-sets must be vetted to be privacy-preserving, fair and free from bias.