PRESTO: Privacy REcommendation and SecuriTy Optimization
PRESTO: Privacy REcommendation and SecuriTy Optimization
A Python package that provides intelligent recommendations for optimal differential privacy algorithms based on user preferences and dataset characteristics. PRESTO uses Bayesian optimization to automatically determine the best privacy preservation algorithm, privacy loss parameters, confidence intervals, and reliability scores for a given dataset.
- Oak Ridge National Laboratory United States
- National Institute of Standards and Technology United States
privacy preservation, data privacy, clinical trials, machine learning, algorithm recommendation, genomics privacy, differential privacy, healthcare privacy, privacy-utility tradeoff, ORNL, Bayesian optimization
privacy preservation, data privacy, clinical trials, machine learning, algorithm recommendation, genomics privacy, differential privacy, healthcare privacy, privacy-utility tradeoff, ORNL, Bayesian optimization
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!