The project goal was to efficiently apply machine-learning techniques on homomorphically-encrypted data and/or models.
Metrics used were ROC, maximal absolute relative difference in prediction probability values, algorithm run time and core utilization.
- Learning how to design algorithms abstractly
- Parallelizing algorithms across multiple cores (Python’s multiprocessing module would not work with Duality’s algorithms).
- Dealing with large quantities of data.
Achievements (according to KPIs)
- Run-times were significantly reduced
- The algorithms are now parallelized (where possible) and modulated.
ROC and maximal absolute relative difference in prediction probability values remained the same despite decreased run times