IBM and Trūata partner to help businesses assess privacy risk in data with advanced Fingerprint technology
Trūata has announced the launch of its partner solution with IBM to help organizations to understand the privacy risk in their data.
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Trūata wins award at the international Privacy-Enhancing Technologies Prize Challenge
Trūata, the privacy-enhanced data analytics solutions provider has been awarded first place in the PETs Prize Red Team Challenge: Advancing Privacy-Preserving Federated Learning
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Trūata Calibrate now available in the Microsoft Azure Marketplace
Trūata, the privacy-enhanced data analytics solutions provider, has announced the availability of Trūata Calibrate in the Microsoft Azure Marketplace.
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Data privacy and digital trust: A balancing act which hinges on the customer
Protecting data privacy and preserving brand integrity can bring about a whole host of unwelcome processes for businesses, with added…
Trūata has been granted a U.S. patent for its innovative approach to measuring and addressing privacy risks
Trūata announces that the USPTO has granted patent approval for the company’s privacy risk quantification and mitigation system and method used in the Trūata Calibrate software solution..
Greater than the sum of its parts: how combined analytics can amplify the value of anonymization
The accelerated adoption of technological innovations to optimise every aspect of our lives not only generates large volumes of data around our activities but also heavily relies on this data. The potential benefits to be gained from the use of this data serves both individuals and organizations.
Trūata granted patent by USPTO that addresses the privacy concerns in mobility data
Trūata has announced the issuance of a new patent from the United States Patent and Trademark Office, further strengthening the foundations of its intellectual property.
Building privacy into AI: is the future federated?
Explore the potential of federated learning – secure data and protect privacy in an AI-driven world. Privacy and security concerns have led the charge for ML technology that can work in a way that preserves consumer privacy, which is why federated learning (FL) has gained such momentum.
How to navigate multiple data privacy regulatory frameworks
Organizations that adopt a proactive, privacy-centric approach will be better positioned to manage regulatory risk.