XOR Platform Highlights
Don’t mask your data or drop any features to preserve privacy. XOR’s data-in-use encryption lets you use all the features to build highly accurate models without compromising data privacy.
Seamlessly integrate with your existing AI pipeline and access sensitive data from anywhere – on-prem or cloud – with XOR’s REST API and Python library.
Encryption-in-use makes collusion virtually impossible. With XOR, data and models are secret shared across parties during computation, safeguarding against quantum attacks.
Never ever trust a third-party to keep your data safe and broker exchanges. With XOR, organizations will never have to transfer data outside their internal firewalls.
Accessing sensitive data securely is no longer a data scientist’s dream. It’s a proven reality. Supports multiple ML use cases from collaborative model development to secure federated learning and cross-border data compliance.
Leverage data while complying with global privacy and data regulations such as CCPA, GDPR, HIPAA, and more.
HOW IT WORKS
Secure Federated Learning
While Federated Learning is flexible and resolves data governance and ownership issues, it does not by itself guarantee security and privacy. Lack of encryption can allow attackers to steal personally identifiable data from the federation devices or communication interfaces. Secure aggregation with XOR insulates FL from various attacks such as Inference or Reconstruction attacks.
Secure MultiParty Computation
Secure MultiParty Computation (MPC) is a cryptographic protocol where multiple parties with privacy-sensitive data can carry out a joint computation while keeping the individual parties’ data secure from adversarial behavior. MPC makes collusion virtually impossible as the adversary would need to access (n-1) parties to decrypt the data.