Homomorphic encryption can be thought of as a set of algorithms that enable computation on encrypted data. On this page we explore the technology in depth.
Tokenization is a process by which a piece of sensitive data, such as a credit card number, is replaced by a surrogate value known as a token. The sensitive data needs to be stored securely. On this page we explore the technology in depth.
Synthetic Data is any data generated by applying a sampling technique to real-world data or by creating simulation scenarios where models interact to create completely new data. On this page we explore the technology in depth.
The aim of federated learning is training a machine learning (ML) algorithm on multiple local datasets contained in local nodes without exchanging data samples. On this page we explore the technology in depth.
The world is filled with valuable information, but organizations must properly handle data from collection to storage to usage. Failing to follow the necessary procedures can result in illegal violations of individual privacy. When people or organizations want to use or share sensitive information, they will turn to manual de-identification, redaction, and/or anonymization.
A security mechanism that executes code in a hardware-based trusted execution environment (TEE), secure enclaves are an emerging approach to data privacy. On this page we explore the technology in depth.
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When it comes to sensitive medical data that includes patient information, privacy is everything. Under the Health Insurance Portability and Accountability Act (HIPAA), healthcare companies that handle their patients’ personal data cannot allow the public access or disclosure of personal information without individual patient consent or knowledge.
Differential privacy is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset, while withholding information about individuals in the dataset. On this page we explore the technology in depth.
A zero-knowledge proof is a mathematical protocol designed to verify information without revealing the information. It permits a “verifying” party to confirm that a “proving” party has some explicit information without that information being revealed by the proving party.
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TripleBlind’s innovations build on well understood principles of data protection. Our innovations radically improve the practical use of privacy preserving technologies, by adding true scalability and faster processing, with support for all data and algorithm types. We support all cloud platforms and unlock the intellectual property value of data, while preserving privacy and enforcing compliance with HIPAA and GDPR.