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…
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.
Privacy-preserving knowledge graphs are data structures that organise and connect information while protecting sensitive or personal data. They use methods like anonymisation, access control, and encryption to ensure that private details are not exposed during data analysis or sharing. This approach helps organisations use the benefits of connected information without risking the privacy of individuals…
Secure data sharing systems are methods and technologies that allow people or organisations to exchange information safely. They use privacy measures and security controls to ensure only authorised users can access or share the data. This helps protect sensitive information from being seen or changed by unauthorised individuals.
Data privacy automation is the use of technology to manage and protect personal information without relying solely on manual processes. Automated systems can identify sensitive data, enforce privacy policies, and ensure compliance with privacy laws by handling tasks like data access requests or deletion automatically. This helps organisations reduce the risk of human error and…