Category: Prompt Engineering

Privacy-Aware Feature Engineering

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

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

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

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…

Differential Privacy Frameworks

Differential privacy frameworks are systems or tools that help protect individual data when analysing or sharing large datasets. They add carefully designed random noise to data or results, so that no single person’s information can be identified, even if someone tries to extract it. These frameworks allow organisations to gain useful insights from data while…

Privacy-Preserving Inference

Privacy-preserving inference refers to methods that allow artificial intelligence models to make predictions or analyse data without accessing sensitive personal information in a way that could reveal it. These techniques ensure that the data used for inference remains confidential, even when processed by third-party services or remote servers. This is important for protecting user privacy…

Zero-Knowledge Machine Learning

Zero-Knowledge Machine Learning is a method that allows someone to prove they have trained a machine learning model or achieved a particular result without revealing the underlying data or the model itself. This approach uses cryptographic techniques called zero-knowledge proofs, which let one party convince another that a statement is true without sharing any of…

Secure Model Training

Secure model training is the process of developing machine learning models while protecting sensitive data and preventing security risks. It involves using special methods and tools to make sure private information is not exposed or misused during training. This helps organisations comply with data privacy laws and protect against threats such as data theft or…