Federated Differential Privacy is a method that combines federated learning and differential privacy to protect individual data during collaborative machine learning. In federated learning, many users train a shared model without sending their raw data to a central server. Differential privacy adds mathematical noise to the updates or results, making it very hard to identify…
Category: Prompt Engineering
Privacy-Preserving Data Sharing
Privacy-preserving data sharing is a way of allowing people or organisations to share information without exposing sensitive or personal details. Techniques such as data anonymisation, encryption, and differential privacy help ensure that shared data cannot be traced back to individuals or reveal confidential information. This approach helps balance the need for collaboration and data analysis…
Oblivious RAM
Oblivious RAM is a technology that hides the pattern of data access in computer memory, so that anyone observing cannot tell which data is being read or written. This prevents attackers from learning sensitive information based on how and when data is accessed, even if they can see all memory requests. It is particularly useful…
Private Set Intersection
Private Set Intersection is a cryptographic technique that allows two or more parties to find common elements in their data sets without revealing any other information. Each party keeps their data private and only learns which items are shared. This method is useful when data privacy is important but collaboration is needed to identify overlaps.
Differential Privacy Guarantees
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…
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…