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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 vid

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 https://hazy.com/product Disclosure: Hazy cofounder.

staticautomatic 7y ago
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.
- thruflo 7y ago
  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.
  - v4dok 7y ago
    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.
    - thruflo 7y ago
      Yes, but starting from the position of “entirely artificial data”.
thom 7y ago
Out of interest, can Hazy learn temporal relationships in streams of data?
- thruflo 7y ago
  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.

staticautomatic 7y ago

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.

thruflo 7y ago
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.
- v4dok 7y ago
  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.
  - thruflo 7y ago
    Yes, but starting from the position of “entirely artificial data”.

thom 7y ago

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

thruflo 7y ago
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.