Collaborate with data partners to train models without exposing, sharing or moving your data.
Data-driven enterprises want access to richer additive external data to build and train their analytical models. However, regulations and internal requirements make data partnerships so complicated that the cost to access this external data renders the data less valuable.
CN-Insight gives you the ability to collaborate with data partners to generate more value from your data assets in a compliant and secure way.
A privacy-protection platform for data science
Secure Multi-Party Computation
Secure Multi-party Computation (SMC), or Multi-Party Computation (MPC), is an approach to jointly compute a function over inputs held by multiple parties while keeping those inputs private, ensuring that no data leaks during computation.
Each computer in the network only sees bits of secret shares — but never anything meaningful. Secret shares are derived from data using correlated randomness such that at the end of the computation, each computer has a share of the solution. The only way to reconstruct the complete solution is to add all the shares together from all the computers involved.
Private Set Intersection
Private Set Intersection (PSI) identifies common elements between datasets typically held by different parties, without revealing anything to each other except the intersection. This replaces simplistic approaches such as one-way hashing functions that are susceptible to dictionary attacks.
Applications for PSI include identifying the overlap with potential data partners (i.e. “Is there a large enough client base in common to be worthwhile to work together), as well as aligning datasets with data partners in preparation for using MPC to train a machine learning model.