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In 2016, the co-founders of Duality Technologies recognized a growing need to protect big data analysis and processing.

Treating, testing, tracing: until an effective coronavirus vaccine is developed, governments around the world are deploying the three Ts as a way out of this crisis. Now anti-money laundering technology may provide a way to secure the last of these without mass infringement of privacy.

The SecurePlus query system allows different businesses to query each others’ systems to gather data—which is vital for mobile payments operations among a host of others—without actually revealing any more data than is strictly necessary to carry out a function.

Duality Technologies, a leading provider of Privacy-Enhancing Technologies (PETs), announced today the launch of  SecurePlus Query,  a privacy-by-design query engine for data collaboration.

Despite fears over privacy, data collection is only increasing. But Duality Technologies promises a new way of analyzing data while it is still encrypted, completely preserving the privacy and security of a data set while also helping businesses learn from its patterns.

With Duality’s technology, enterprises can securely collaborate applying advanced analyses and Artificial Intelligence to data while it is encrypted, generating insights without ever exposing the raw data.

Samsung SDS and Duality Technologies convened AI research and industry leaders last week at the Microsoft campus in Redmond for a workshop focused on advancing standards for Homomorphic Encryption (HE).

Duality Technologies was founded about three years ago on the premise that data privacy would become increasingly important for individuals, companies and public organizations.

Data privacy is an increasing concern for companies and individuals. Techrepublic discussed with Duality’s Rina Shainski what’s on the landscape for 2020.

Shafi Goldwasser, chief scientist at Duality Technologies who is the 2012 recipient of the Turing Award, told The Wall Street Journal that new cryptography advances allow sharing of sensitive data without fear of theft or other privacy violations.