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I work on this problem full-time, that’s why we create synthetic data: https://medium.com/capital-one-tech/why-you-dont-necessarily-need-data-for-data-science-

I work on this problem full-time, that’s why we create synthetic data: https://medium.com/capital-one-tech/why-you-dont-necessarily-need-data-for-data-science-48d7bf503074 https://medium.com/capital-one-tech/why-you-dont-necessarily... It’s even good enough in many cases to build models off of. However, it’s important to not that numbers such as SSNs are always “real”, in the sense they link to someone. A random SSN has with near certainty been used before and belonged to someone (perhaps multiple) people. The trick is ensuring the rest of the attributes don’t match the individual.

nixpulvis 7y ago
I'm sorry, but if you're generating the inputs to your model based on a model, you've invented a pathological chicken and egg problem. I suppose as long as your model never changes this will work well, but what happens when you want to change your model, but already have stored synthetic data? Tangent: I suppose humans choice in models and statistics to use can be argued to be a similar thing, but I'm still dubious of how this concept is being communicated.
- lettergram 7y ago
  Take a dataset, extract the features, send to model, design model, check if we match desired output. That’s the standard Data science workflow. What we do is extract features in an unsupervised way and build a model to recreate the dataset from said feature. When the synthesized dataset is indistinguishable from the real thing. You should be able to build a model that gets you 90%-100% of the way there with synthetic data, then tune / retrain on the real data. The model that does decisioning itself doesn’t matter. If you needed to adjust the synthetic dataset, you’d have to either build a new generative model OR otherwise weight the dataset.
ThePhysicist 7y ago
While I think data synthesis is a useful technique I never understood how most synthetization approaches can so confidently claim that there is no leakage of sensitive information into the synthesis model. The only approach I've seen for this is using differential privacy techniques to limit the amount of information that can be learned by the synthesis model from a given datapoint, but doing so also drastically limits the ability of the model to learn from the input data. Another issue I have with data synthetization is that by trying to reproduce the original data as faithfully as possible you waste a lot of your privacy budget on meaningless features. That said I'm happy to be convinced otherwise. Do you have a publicly available demo dataset that I could look at?
- deleted 7y ago
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nixpulvis 7y ago

I'm sorry, but if you're generating the inputs to your model based on a model, you've invented a pathological chicken and egg problem. I suppose as long as your model never changes this will work well, but what happens when you want to change your model, but already have stored synthetic data? Tangent: I suppose humans choice in models and statistics to use can be argued to be a similar thing, but I'm still dubious of how this concept is being communicated.

lettergram 7y ago
Take a dataset, extract the features, send to model, design model, check if we match desired output. That’s the standard Data science workflow. What we do is extract features in an unsupervised way and build a model to recreate the dataset from said feature. When the synthesized dataset is indistinguishable from the real thing. You should be able to build a model that gets you 90%-100% of the way there with synthetic data, then tune / retrain on the real data. The model that does decisioning itself doesn’t matter. If you needed to adjust the synthetic dataset, you’d have to either build a new generative model OR otherwise weight the dataset.

ThePhysicist 7y ago

While I think data synthesis is a useful technique I never understood how most synthetization approaches can so confidently claim that there is no leakage of sensitive information into the synthesis model. The only approach I've seen for this is using differential privacy techniques to limit the amount of information that can be learned by the synthesis model from a given datapoint, but doing so also drastically limits the ability of the model to learn from the input data. Another issue I have with data synthetization is that by trying to reproduce the original data as faithfully as possible you waste a lot of your privacy budget on meaningless features. That said I'm happy to be convinced otherwise. Do you have a publicly available demo dataset that I could look at?

deleted 7y ago
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