Duality Technologies Launches Platform for Analyzing Big Data While Keeping It Private

Publié le by Coindesk | Publié le

While it may sound dry, it marks a step forward in practical uses of HE, which lets multiple actors conduct data analysis on a variety of datasets while keeping that information encrypted and protecting things like personally identifiable information.

The result of the calculations is also encrypted, but when the result is decrypted it is identical to the result had the data not been encrypted in the first place.

So if data is sent to a commercial cloud, large-scale analysis can be done on it without putting sensitive information such as people's medical or financial information at risk.

In traditional forms of encryption, data is only protected in storage and during communications.

"Homomorphic Encryption allows multiple parties to collaborate on data without seeing each other's data assets, thus generating valuable insights from them."

In a world where privacy concerns are advancing, particularly amid the pandemic, and disparate privacy laws are resulting in countries revoking some form of data access to others, tools like HE could give companies a way to get data insights without creating the potential not just for non-compliance, but also for big data abuse that has driven concerns about Big Tech.

Earlier this year researchers showed how HE can enable analysis on genomic data in such a way that it preserves data privacy.

Duality piloted SecurePlus Statistics at the Tel Aviv Sourasky Medical Center in Israel where it was used to analyze data regarding the prevention, diagnosis and treatment of cancer studies while protecting personal health information.

"Previous blockchain-based IoT systems have issues related to privacy leakage of sensitive information to the servers as the servers can access the plaintext data from the IoT devices," reads the abstract.

"So, we present the potential of integration of blockchain based-IoT with homomorphic encryption that can secure the IoT data with high privacy in a decentralized mode."