Privacy Enhancing Technologies (PETS) are a set of tools, methodologies and techniques that are designed to protect the privacy of individuals and their personal data.

FHE stands for Fully Homomorphic Encryption, which is a type of encryption scheme that enables computation on ciphertexts directly, without the need for decryption.

Federated Learning is a machine learning technique that trains an algorithm across multiple decentralized edge devices.

A Trusted Execution Environment (TEE) is a secure area within a computer system or mobile device that ensures the confidentiality and integrity of data and processes that are executed inside it.

Multiparty computation (MPC) is a technique in cryptography that enables multiple parties to jointly compute a function over their private inputs without revealing those inputs

Differential Privacy is a privacy-enhancing technique that allows organizations to collect and analyze data while preserving the privacy of the individuals in the dataset.