
Glossary
Your guide to the terms, tools and (many!) acronyms redefining privacy, compliance, and secure collaboration.
Core cryptographic concepts
Fully Homomorphic Encryption (FHE)
A revolutionary cryptographic method that allows computations to be performed directly on encrypted data without decrypting it, ensuring sensitive information remains secure during processing.
Secure Multi-Party Computation (MPC)
A cryptographic method enabling multiple parties to compute a result collaboratively without revealing their individual inputs, ideal for sensitive data-sharing scenarios.
Zero-Knowledge Proofs (ZKP)
A cryptographic technique allowing one party to prove they know something (e.g., a password) without revealing the actual information itself.
Encryption-in-Use
Techniques like FHE that secure data even while it’s being processed, unlike traditional encryption methods that only protect data at rest or in transit.
Privacy & security technologies
Privacy Enhancing Technologies (PETs)
A suite of tools and techniques designed to safeguard privacy while enabling secure data use and sharing. PETs include FHE, differential privacy, secure multi-party computation, and more.
Differential privacy
A privacy-preserving method that adds statistical noise to datasets, ensuring individuals’ data remains unidentifiable while allowing meaningful analysis.
Data anonymisation
The process of removing or modifying personal identifiers from data to protect individual privacy while retaining its analytical value.
Applications of Privacy and Security Technologies
Encrypted search
The ability to search encrypted datasets without revealing the search terms or the underlying data, enabled by technologies like FHE.
Trustless collaboration
Systems where parties collaborate securely without needing to fully trust one another, made possible through technologies like FHE and SMPC.
Data portability and interoperability
Securely sharing and processing data across systems, industries, and borders using encryption and PETs.
AI and secure model training
Leveraging PETs to train machine learning models on sensitive datasets while maintaining privacy and regulatory compliance.

