Our application is for communications efficient set reconciliation to convert Bitcoin's quadratic-overhead transaction gossip protocol (O(txn*peers)) to effectively linear (O(txn)), though the primary academic work that our work was based on were concerned with fuzzy extractors for privacy preserving (and encryption key generating) biometrics.
Comments
The minisketch library I worked on can be used for near optimal (in the sense of information leak) error correction for "set like" features:
https://github.com/sipa/minisketch/
Our application is for communications efficient set reconciliation to convert Bitcoin's quadratic-overhead transaction gossip protocol (O(txn*peers)) to effectively linear (O(txn)), though the primary academic work that our work was based on were concerned with fuzzy extractors for privacy preserving (and encryption key generating) biometrics.
For more info on fuzzy extractors from biometrics see also this great paper (and the linked survey): http://www.cs.bu.edu/~reyzin/fuzzy.html