Secure data federation is a way of combining information from different sources without moving or copying the data. It lets users access and analyse data from multiple places as if it were all in one location, while keeping each source protected. Security measures ensure that only authorised people can view or use the data, and…
Data privacy frameworks are organised sets of guidelines and rules designed to help organisations manage and protect personal data. They outline how data should be collected, stored, shared, and deleted to ensure individual privacy rights are respected. These frameworks often help businesses comply with local or international laws and reassure customers that their information is…
Privacy-preserving data mining is a set of techniques that allow useful patterns or knowledge to be found in large data sets without exposing sensitive or personal information. These methods ensure that data analysis can be done while keeping individuals’ details confidential, even when data is shared between organisations. It protects peoplenulls privacy by masking, encrypting,…
Secure Multi-Party Analytics is a method that allows several organisations or individuals to analyse shared data together without revealing their private information to each other. It uses cryptographic techniques to ensure that each party’s data remains confidential during analysis. This approach enables valuable insights to be gained from combined data sets while respecting privacy and…
Secure data anonymisation is the process of removing or altering personal information from datasets so that individuals cannot be identified. This helps protect peoplenulls privacy while still allowing the data to be used for analysis or research. Techniques include masking names, scrambling numbers, or removing specific details that could reveal someonenulls identity.
Identity verification systems are tools or processes used to confirm that someone is who they say they are. These systems check personal details like names, addresses, or official documents, and may use biometrics such as fingerprints or facial recognition. Their main goal is to prevent fraud and protect sensitive information by making sure only authorised…
Privacy-aware feature engineering is the process of creating or selecting data features for machine learning while protecting sensitive personal information. This involves techniques that reduce the risk of exposing private details, such as removing or anonymising identifiable information from datasets. The goal is to enable useful data analysis or model training without compromising individual privacy…
Secure Data Sharing Frameworks are systems and guidelines that allow organisations or individuals to share information safely with others. These frameworks make sure that only authorised people can access certain data, and that the information stays private and unchanged during transfer. They use security measures like encryption, access controls, and monitoring to protect data from…
Homomorphic encryption models are special types of encryption that allow data to be processed and analysed while it remains encrypted. This means calculations can be performed on encrypted information without needing to decrypt it first, protecting sensitive data throughout the process. The result of the computation, once decrypted, matches what would have been obtained if…
Secure Multi-Party Learning is a way for different organisations or individuals to train machine learning models together without sharing their raw data. This method uses cryptographic techniques to keep each party’s data private during the learning process. The result is a shared model that benefits from everyone’s data, but no participant can see another’s sensitive…