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Comment by kick | Hacker News Reader

“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.

Anyone taking bets on how long it's going to be before these idiots end up leaking the SSN of every US citizen because their categorizer failed?

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Too negative my dude.

How do you know they're idiots? Maybe you're the idiot

You don't have to be a machine learning expert to understand that no classifier is going to be correct 100% of the time. The laws against divulging PII don't contain exceptions for classifiers goofing.

And you don't have to present criticism by calling a group of people you don't know idiots.

That’s not how it works. Synthetic data is entirely artificial rather than transformed, so you’re not worried about “missing some PII”.

See for example the videos on https://hazy.com/product

Disclosure: Hazy cofounder.

Why would you need "to anonymize vast amounts of data — so that it’s no longer tied to customer information" or "appl[y] access policies" if the data contain no PII? Presumably the ML is anonymizing the data and the access policies are necessary because the data contain PII.

Yup, people tend to confuse concepts and refer to synthetic data as anonymised data. They are very different things.

Anonymised data or redacted data are transformations of a data set that _hopes_ not to leak too much PII / sensitive data. People don’t use ML to anonymise but they do use ML to classify as a first step before splatting or generalising.

In that case, its absolutely right that the ML classifier not being 100% results in PII leaking.

This is a key reason why anonymisation and redaction are widely seen as problematic and are being replaced by synthetic data and, maybe in future, homomorphic encryption.

Homomorphic encryption and any encryption in-use technology is no guarantee of privacy on its own. Synthetic data has the same dillema of utility vs anonymity as any other anonymization tech.

Yes, but starting from the position of “entirely artificial data”.

Out of interest, can Hazy learn temporal relationships in streams of data?

Yup. We focus on financial services, so time series data is a key requirement / capability.

There are nuances — like static vs dynamic distributions and whether the downstream model is actually just aggregating anyway.

I’m my username at hazy.com if you’d like to connect.

Synthetic data uses the distribution(s) of the underlying dataset(s) to generate a totally new dataset that in theory has the same statistical properties of the original dataset. The rub is in making sure that you're actually synthesizing a valid statistical representation of the original dataset, including joint distributions. Otherwise, you wind up with models that won't generalize back to the original data.

To the extent that the synthetic data comes from a generative model, why not sell the parameters of the generative model?

It's being asked a lot here. The answer is that this "generative model" is a utopia

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