Startup Gretel Building Anonymized Data Platform
March 19, 2020
There is a lot of valuable but sensitive data out there that developers and engineers would love to get their innovative hands on, but it is difficult to impossible for them to access. Until now.
Enter Gretel, a startup working to anonymize confidential data. We learn about the upcoming platform from Inventiva’s article, “A Group of Ex-NSA And Amazon Engineers Are Building a ‘GitHub for Data’.” Co-founders Alex Watson, John Myers, Ali Golshan, and Laszlo Bock were inspired by the source code sharing platform GitHub. Reporter surbhi writes:
“Often, developers don’t need full access to a bank of user data — they just need a portion or a sample to work with. In many cases, developers could suffice with data that looks like real user data. … ‘We’re building right now software that enables developers to automatically check out an anonymized version of the data set,’ said Watson. This so-called ‘synthetic data’ is essentially artificial data that looks and works just like regular sensitive user data. Gretel uses machine learning to categorize the data — like names, addresses and other customer identifiers — and classify as many labels to the data as possible. Once that data is labeled, it can be applied access policies. Then, the platform applies differential privacy — a technique used to anonymize vast amounts of data — so that it’s no longer tied to customer information. ‘It’s an entirely fake data set that was generated by machine learning,’ said Watson.”
The founders are not the only ones who see the merit in this idea; so far, the startup has raised $3.5 million in seed funding. Gretel plans to charge users based on consumption, and the team hopes to make the platform available within the next six months.
Cynthia Murrell, March 19, 2020
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