Privacy-preserving knowledge graphs are data structures that organise and connect information while protecting sensitive or personal data. They use methods like anonymisation, access control, and encryption to ensure that private details are not exposed during data analysis or sharing. This approach helps organisations use the benefits of connected information without risking the privacy of individuals…
Category: Privacy-Preserving Technologies
Secure Model Inference
Secure model inference refers to techniques and methods used to protect data and machine learning models during the process of making predictions. It ensures that sensitive information in both the input data and the model itself cannot be accessed or leaked by unauthorised parties. This is especially important when working with confidential or private data,…
Blockchain Data Integrity
Blockchain data integrity means ensuring that information stored on a blockchain is accurate, complete, and cannot be changed without detection. Each piece of data is linked to the previous one using cryptographic methods, creating a secure chain of records. This makes it nearly impossible to alter past information without the change being obvious to everyone…
Secure Data Sharing Systems
Secure data sharing systems are methods and technologies that allow people or organisations to exchange information safely. They use privacy measures and security controls to ensure only authorised users can access or share the data. This helps protect sensitive information from being seen or changed by unauthorised individuals.
Secure Data Collaboration Systems
Secure data collaboration systems are tools or platforms that let multiple people or organisations work together on shared information without risking the privacy or safety of that data. These systems use protections like encryption, access controls, and monitoring to make sure only authorised users can see or change the data. This helps groups share sensitive…
Data Privacy Automation
Data privacy automation is the use of technology to manage and protect personal information without relying solely on manual processes. Automated systems can identify sensitive data, enforce privacy policies, and ensure compliance with privacy laws by handling tasks like data access requests or deletion automatically. This helps organisations reduce the risk of human error and…
Privacy-Preserving Data Analysis
Privacy-preserving data analysis refers to techniques and methods that allow people to analyse and gain insights from data without exposing sensitive or personal information. This approach is crucial when dealing with data that contains private details, such as medical records or financial transactions. By using special tools and methods, organisations can extract useful information while…
Secure Multi-Party Computation
Secure Multi-Party Computation, often abbreviated as MPC, is a method that allows several people or organisations to work together on a calculation or analysis without sharing their private data with each other. Each participant keeps their own information secret, but the group can still get a correct result as if they had combined all their…
Quantum-Resistant Algorithms
Quantum-resistant algorithms are cryptographic methods designed to stay secure even if powerful quantum computers are developed. Traditional encryption, like RSA and ECC, could be broken by quantum computers using advanced techniques. Quantum-resistant algorithms use different mathematical problems that are much harder for quantum computers to solve, helping to protect sensitive data into the future.
Data Anonymization Pipelines
Data anonymisation pipelines are systems or processes designed to remove or mask personal information from data sets so individuals cannot be identified. These pipelines often use techniques like removing names, replacing details with codes, or scrambling sensitive information before sharing or analysing data. They help organisations use data for research or analysis while protecting people’s…