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…
Category: Privacy-Preserving Technologies
Federated Learning Protocols
Federated learning protocols are rules and methods that allow multiple devices or organisations to train a shared machine learning model without sharing their private data. Each participant trains the model locally on their own data and only shares the updates or changes to the model, not the raw data itself. These protocols help protect privacy…
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…
Encrypted Neural Networks
Encrypted neural networks are artificial intelligence models that process data without ever seeing the raw, unprotected information. They use encryption techniques to keep data secure during both training and prediction, so sensitive information like medical records or financial details stays private. This approach allows organisations to use AI on confidential data without risking exposure or…
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…
Secure Model Aggregation
Secure model aggregation is a process used in machine learning where updates or results from multiple models or participants are combined without revealing sensitive information. This approach is important in settings like federated learning, where data privacy is crucial. Techniques such as encryption or secure computation ensure that individual contributions remain private during the aggregation…
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 Collaboration Tools
Secure collaboration tools are digital platforms or applications that allow people to work together while keeping their shared information safe from unauthorised access. They provide features like encrypted messaging, secure file sharing, and controlled access to documents. These tools help teams communicate and collaborate efficiently, even when working remotely or across different locations, without compromising…
Secure File Sharing
Secure file sharing is the process of sending digital files to others in a way that protects the information from unauthorised access. It uses methods like encryption, password protection, and access controls to keep data safe while being shared. This helps individuals and organisations ensure that only intended recipients can view or download sensitive documents.