LeapYear

About LeapYear

Differential privacy is a mathematically proven standard of data privacy that ensures all data can be used for analytics and machine learning without the risk of compromising information about individual records. LeapYear’s differentially private system protects some of the world’s most sensitive datasets, including social media data, medical information, and financial transactions. The system ensures analysts, data scientists, and researchers can derive value from all of the data, including data of highly sensitive fields, while protecting all facts about individuals, entities, and transactions. Traditional approaches, such as aggregation, anonymization, or masking degrade data value and can be easily exploited to reconstruct sensitive information. LeapYear’s implementation of differential privacy provides mathematically proven assurances that information about individual records cannot be reconstructed, while also enabling all of the data to be leveraged for reporting