Secure data sharing is the process of exchanging information between people, organisations, or systems in a way that protects the data from unauthorised access, misuse, or leaks. It involves using tools and techniques like encryption, permissions, and secure channels to make sure only the intended recipients can see or use the information. This is important…
Privacy-preserving analytics refers to methods and technologies that allow organisations to analyse data and extract useful insights without exposing or compromising the personal information of individuals. This is achieved by using techniques such as data anonymisation, encryption, or by performing computations on encrypted data so that sensitive details remain protected. The goal is to balance…
Privacy-preserving feature models are systems or techniques designed to protect sensitive information while building or using feature models in software development or machine learning. They ensure that personal or confidential data is not exposed or misused during the process of analysing or sharing software features. Approaches often include methods like data anonymisation, encryption, or computation…
Secure data collaboration is a way for people or organisations to work together using shared data while keeping that data protected. It involves using tools and processes that make sure sensitive information is not exposed to anyone who should not see it. This often includes encryption, access controls, and monitoring to ensure that data stays…
Homomorphic inference models allow computers to make predictions or decisions using encrypted data without needing to decrypt it. This means sensitive information can stay private during processing, reducing the risk of data breaches. The process uses special mathematical techniques so that results are accurate, even though the data remains unreadable during computation.
Multi-Party Inference Systems allow several independent parties to collaborate on using artificial intelligence or machine learning models without directly sharing their private data. Each party contributes their own input to the system, which then produces a result or prediction based on all inputs while keeping each party’s data confidential. This approach is commonly used when…
Encrypted model processing is a method where artificial intelligence models operate directly on encrypted data, ensuring privacy and security. This means the data stays protected throughout the entire process, even while being analysed or used to make predictions. The goal is to allow useful computations without ever exposing the original, sensitive data to the model…
Differential privacy metrics are methods used to measure how much private information might be exposed when sharing or analysing data. They help determine if the data protection methods are strong enough to keep individuals’ details safe while still allowing useful insights. These metrics guide organisations in balancing privacy with the usefulness of their data analysis.
Privacy-aware inference systems are technologies designed to make predictions or decisions from data while protecting the privacy of individuals whose data is used. These systems use methods that reduce the risk of exposing sensitive information during the inference process. Their goal is to balance the benefits of data-driven insights with the need to keep personal…
Secure knowledge aggregation is a process that combines information from multiple sources while protecting the privacy and security of the data. It ensures that sensitive details remain confidential during collection and analysis. This approach is important when information comes from different parties who may not want to share all their data openly.