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Privacy-enhancing technologies (PETs) explained
What are PETs?
A set of technologies which mitigate the risk to the individual of data privacy abuse.
Trusted Execution Environments
A mechanism for enabling multiple parties to collaboratively do computation.
Tokenization
A process through which one substitutes a sensitive identifier (e.g., a unique ID number or other PII) with a non-sensitive equivalent (i.e., a ‘token’) that has no extrinsic or exploitable meaning or value.
Federated Machine Learning
A Machine Learning (ML) setting where many clients collaboratively train a model in a decentralised manner.
Homomorphic Encryption
Encryption schemes which allow mathematical operations to be performed on the underlying data whilst keeping the data in the encrypted space.
Secure Multi-Party Computation
A cryptographic protocol that distributes a computation across multiple parties where no individual party can see the other parties’ data.
Federated ML Model Evaluation
A method for sending a trained ML model you wish to evaluate to the data, rather than requiring the data to be centralised first.
Synthetic Data Generation
A computer-generated dataset sufficiently similar to an original base dataset.
Differential Privacy
A mechanism for providing rigorous, statistical guarantees against what an adversary can infer from learning the result of some randomised algorithm.
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