HIPAA compliance software is digital technology designed to help organisations meet the requirements of the Health Insurance Portability and Accountability Act (HIPAA). This software helps protect sensitive patient health information by managing data security, access controls, and documentation. It often includes features like risk assessments, audit logging, and secure messaging to ensure healthcare providers and…
Category: Prompt Engineering
GDPR Compliance Software
GDPR compliance software is a tool or set of tools designed to help organisations follow the rules set by the General Data Protection Regulation, a law in the European Union that protects people’s personal data. This software assists businesses in managing how they collect, store, use, and share personal information, making sure they respect privacy…
Data Loss Prevention
Data Loss Prevention, or DLP, refers to a set of tools and processes designed to stop sensitive information from being lost, misused or accessed by unauthorised people. DLP systems monitor and control data as it moves across networks, is stored, or is used on devices. The goal is to make sure important information, such as…
Private Data Querying
Private data querying is a way to search or analyse sensitive data without exposing the actual information to others. It uses specialised techniques to keep the content of the data hidden, even from the person or system performing the query. This helps maintain privacy and security while still allowing useful insights to be gained from…
Secure Data Aggregation
Secure data aggregation is a process that combines data from multiple sources while protecting the privacy and security of the individual data points. It ensures that sensitive information is not exposed during collection or processing. Methods often include encryption or anonymisation to prevent unauthorised access or data leaks.
Privacy-Preserving Feature Engineering
Privacy-preserving feature engineering refers to methods for creating or transforming data features for machine learning while protecting sensitive information. It ensures that personal or confidential data is not exposed or misused during analysis. Techniques can include data anonymisation, encryption, or using synthetic data so that the original private details are kept secure.
Federated Differential Privacy
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…
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.