Differential privacy guarantees are assurances that a data analysis method protects individual privacy by making it difficult to determine whether any one person’s information is included in a dataset. These guarantees are based on mathematical definitions that limit how much the results of an analysis can change if a single individual’s data is added or…
Category: Prompt Engineering
Encrypted Machine Learning
Encrypted machine learning is a method where data is kept secure and private during the process of training or using machine learning models. This is done by using encryption techniques so that data can be analysed or predictions can be made without ever revealing the raw information. It helps organisations use sensitive information, like medical…
Private Data Federation
Private Data Federation is a way for different organisations to analyse and share insights from their separate data sets without actually moving or exposing the raw data to each other. This approach uses secure techniques so that each party keeps control of its own information while still being able to collaborate on analysis. It is…
Secure Multi-Party Analytics
Secure Multi-Party Analytics is a method that allows several organisations or individuals to analyse data together without sharing their private information. Each participant keeps their own data confidential while still being able to contribute to the overall analysis. This is achieved using cryptographic techniques that ensure no one can see the raw data of others,…
Privacy Pools
Privacy Pools are cryptographic protocols that allow users to make private transactions on blockchain networks by pooling their funds with others. This method helps hide individual transaction details while still allowing users to prove their funds are not linked to illicit activities. Privacy Pools aim to balance the need for personal privacy with compliance and…
Privacy-Preserving Smart Contracts
Privacy-preserving smart contracts are digital agreements that run on blockchains while keeping user data and transaction details confidential. Unlike regular smart contracts, which are transparent and visible to everyone, these use advanced cryptography to ensure sensitive information stays hidden. This allows people to use blockchain technology without exposing their personal or business details to the…
Differential Privacy in Blockchain
Differential privacy is a technique that protects the privacy of individuals in a dataset by adding mathematical noise to the data or its analysis results. In blockchain systems, this method can be used to share useful information from the blockchain without revealing sensitive details about specific users or transactions. By applying differential privacy, blockchain projects…
Secure Aggregation
Secure aggregation is a technique that allows multiple parties to combine their data so that only the final result is revealed, and individual contributions remain private. This is especially useful when sensitive information needs to be analysed collectively without exposing any single person’s data. It is often used in distributed computing and privacy-preserving machine learning…
Data Masking
Data masking is a process used to hide or obscure sensitive information within a database or dataset, so that only authorised users can see the real data. It replaces original data with fictional but realistic values, making it unreadable or useless to unauthorised viewers. This helps protect personal, financial, or confidential information from being exposed…
Data Loss Prevention (DLP)
Data Loss Prevention (DLP) refers to a set of tools and processes designed to stop sensitive data from being lost, leaked, or accessed by unauthorised people. It monitors how data is used, moved, and shared within an organisation and outside of it. DLP systems can automatically block, alert, or encrypt data when a risk is…