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.
Category: Prompt Engineering
Homomorphic Data Processing
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…
Encrypted Feature Processing
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…
Differential Privacy Optimization
Differential privacy optimisation is a process of adjusting data analysis methods so they protect individuals’ privacy while still providing useful results. It involves adding carefully controlled random noise to data or outputs to prevent someone from identifying specific people from the data. The goal is to balance privacy and accuracy, so the information remains helpful…
Privacy-Aware Model Training
Privacy-aware model training is the process of building machine learning models while taking special care to protect the privacy of individuals whose data is used. This involves using techniques or methods that prevent the model from exposing sensitive information, either during training or when making predictions. The goal is to ensure that personal details cannot…
Privacy-Preserving Model Updates
Privacy-preserving model updates are techniques used in machine learning that allow a model to learn from new data without exposing or sharing sensitive information. These methods ensure that personal or confidential data remains private while still improving the modelnulls performance. Common approaches include encrypting data or using algorithms that only share necessary information for learning,…
Secure Data Integration
Secure Data Integration is the process of combining data from different sources while ensuring the privacy, integrity, and protection of that data. This involves using technologies and methods to prevent unauthorised access, data leaks, or corruption during transfer and storage. The goal is to make sure that data from different systems can work together safely…
Data Privacy Compliance
Data privacy compliance means following laws and rules that protect how personal information is collected, stored, used, and shared. Organisations must make sure that any data they handle is kept safe and only used for approved purposes. Failure to comply with these rules can lead to fines, legal trouble, or loss of customer trust.
Secure Data Collaboration
Secure data collaboration allows people or organisations to work together on shared data while keeping that data safe from unauthorised access. It uses technology and rules to protect sensitive information, ensuring only approved users can view or change data. This is important when teams from different companies or departments need to cooperate but must follow…
Blockchain-Based Identity Systems
Blockchain-based identity systems use blockchain technology to create and manage digital identities in a secure and decentralised way. Instead of storing personal data on a single server, information is recorded across a distributed network, making it harder for hackers to tamper with or steal sensitive data. These systems often give users more control over their…