USE ALL YOUR DATA
Never move or
modify your data
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, statistical analysis, and machine learning.
Our patented platform is at the forefront of maximizing data utility while ensuring mathematically proven, ironclad privacy protection.
Traditional approaches to protect privacy reduce value by redacting, masking, or aggregating data. With LeapYear, our differentially private machine learning platform allows analysts to generate insights from all data, including the most sensitive fields. The system executes across the entire dataset, introducing precisely calibrated randomization at the compute level to prevent against the exfiltration or reconstruction of sensitive, record-level properties.
LeapYear maintains the integrity of data, eliminating the inefficiency and loss of fidelity seen with traditional privacy approaches. With LeapYear, all the data are used for generating insights, yet none of the sensitive information is exposed.
LeapYear simplifies data access, eliminating the need to move data for analysis. Differentially private analytics enable distributed insights on data assets, while respecting regulatory and confidentiality requirements including geographical and organizational boundaries. LeapYear eliminates costly and time-consuming processes associated with unifying widely distributed, sensitive data, leading to faster and more valuable insights.
LeapYear has scaled to environments with over 20 petabytes of data, supporting
interactive analytics for large user bases.
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Ishaan Nerurkar, CEO of LeapYear, explores the process of taking differential privacy from a mathematical theory to an enterprise class platform.