There is a well documented paradox in trading alpha for quantitative strategies; the buyer wants to evaluate the data to see how it can improve their models before buying it, while the seller doesn't want to give away their IP without being paid. Traditional methods of providing sample sets or older historical data can lead to over-fitting and poor prediction when applied to new data streams. How can correlation and causality be calculated without revealing the inputs? How do you monetize insights from proprietary trading information without giving away the data?
"Trading data is tedious" - Alexander Linden, Gartner (from The Economist)
Investment banks, hedge funds and third party data providers leverage Secret Computing™ capabilities to efficiently calculate the impact of private data sources on existing models to determine its value, without exposing the data. The XOR Engine provides arithmetic precision that yields the same results as plaintext calculations, even with complex machine learning algorithms running on massive datasets. Computation can be on-premise or secretly shared to cloud providers for zero-knowledge analytics, with the results securely delivered only to predefined recipients.