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Privacy-Enhancing Technologies (PETs) can strike a balance between public health and privacy by enabling privacy-enhanced information sharing without exposing individuals’ private data.

Researchers affiliated with Duality Technologies have published a study on applying a privacy-preserving AI technique — homomorphic encryption (HE) — to large-scale, genome-wide association studies (GWAS) on genetic and phenotype data.

Computer scientist Shafi Goldwasser’s startup Duality Technologies develops a technology that allows authorities to monitor the spread of coronavirus and gain meaningful insights from encrypted data, without compromising the privacy of citizens

As apps tracing Covid-19 spring up, there are fears for the safety of our private data. But Shafi Goldwasser may have the solution.

Crisis and the uncertainty and panic that accompany it often opens doors to criminality, inviting bad actors to prey upon our fears and anxieties.

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.