Key rotation strategies are methods for changing cryptographic keys regularly to maintain security. By replacing old keys with new ones, organisations reduce the risk of keys being compromised. Key rotation can be scheduled automatically or triggered by specific events, such as suspected breaches or policy changes.
Data encryption optimisation involves improving the speed, efficiency, and effectiveness of encrypting and decrypting information. It aims to protect data without causing unnecessary delays or using excessive computing resources. Techniques include choosing the right algorithms, reducing redundant steps, and balancing security needs with performance requirements.
Blockchain privacy protocols are sets of rules and technologies designed to keep transactions and user information confidential on blockchain networks. They help prevent outsiders from tracing who is sending or receiving funds and how much is being transferred. These protocols use cryptographic techniques to hide details that are normally visible on public blockchains, making it…
Decentralised identity verification is a way for people to prove who they are online without relying on a single company or authority to manage their information. Instead, individuals control their own identity data and can share only what is needed with others. This approach uses secure technologies, often including blockchain, to make sure identity claims…
Privacy-preserving analytics refers to methods and tools that allow organisations to analyse data while protecting the privacy of individuals whose information is included. These techniques ensure that sensitive details are not exposed, even as useful insights are gained. Approaches include anonymising data, using secure computation, and applying algorithms that limit the risk of identifying individuals.
Secure knowledge sharing is the process of exchanging information or expertise in a way that protects it from unauthorised access, loss or misuse. It involves using technology, policies and practices to ensure that only the right people can view or use the shared knowledge. This can include encrypting documents, controlling user access, and monitoring how…
Homomorphic data processing is a method that allows computations to be performed directly on encrypted data, so the data never needs to be decrypted for processing. This means sensitive information can be analysed and manipulated without exposing it to anyone handling the computation. It is especially useful for privacy-sensitive tasks where data security is a…
Federated learning optimisation is the process of improving how machine learning models are trained across multiple devices or servers without sharing raw data between them. Each participant trains a model on their own data and only shares the learned updates, which are then combined to create a better global model. Optimisation in this context involves…
Multi-Party Model Training is a method where several independent organisations or groups work together to train a machine learning model without sharing their raw data. Each party keeps its data private but contributes to the learning process, allowing the final model to benefit from a wider range of information. This approach is especially useful when…
Encrypted feature processing is a technique used to analyse and work with data that has been encrypted for privacy or security reasons. Instead of decrypting the data, computations and analysis are performed directly on the encrypted values. This protects sensitive information while still allowing useful insights or machine learning models to be developed. It is…