Sounds very similar to homomorphic encryption, except with no compromise in performance.
I wonder if this the technique behind Numerai
Artificial data give the same results as real data without compromising privacy
21–30 of 48 posts
Re: Artificial data give the same results as real data without compromising privacy
#22How is this related to and different from differential privacy ?
Differential privacy is a formal guarantee of an algorithm. Roughly, given algorithm A that takes input database X, we say A is differentially private if m, for any X' differing in at most one row from X, the output distributions of A(X) and A(X') are similar. So to say an algorithm is differentially private you need to prove a claim like this. It's hard to compare to this paper, because this paper's privacy claims a…
To add to this answer: the methods outlined in the paper allow for perfect reconstruction of the underlying data in many cases, as the simulation of data is simply sampling from fitted distributions.
Re: Artificial data give the same results as real data without compromising privacy
#23I haven't read the original paper (yet), but something doesn't sit right with the work, if the way it is portrayed is indeed faithful to it and I'm not missing something important. - It looks like the work of the data scientists will be limited to the extent of the modeling already done by recursive conditional parameter aggregation. (edit: So why not just ship that model and adapt it instead of using it to generate…
Just finished the paper, so let me take a stab:
Peeling back the mystery a bit, what is happening is:
1. From each child table upwards, model each column as a simple distribution (e.g. Gaussian) and covariance matrix.
2. Given those child table distribution parameters, pass them back as row values to their respective parent tables.
What you end up with is a "flattened" version of each parent table that has the information (in an "information theoretic" sense) of all child relations. Sampling from distributions is straight forward. The stats methods are outlined in section 3 of the paper.
Things of note:
- The paper makes heavy use of Copula transformations to normalize data whenever it passes around the distribution parameters.
- It deals with missing values by adding something like a dummy column.
- The key insight is that columns must be represented by parameterized distributions, but they don't have to be Gaussian. The Kolmogrov-Smirnov test is used to choose the "best fit" CDF to model.
To your question about the role of the data scientists: they are using the resulting simulations to solve more complex tasks. The goal of the experiment was to see how well the sample data would perform against Kaggle competitions. So I guess the idea was that if winners were indistinguishable, the simple/hierarchical distributions would be considered robust enough for complex tasks. In the end, I'm sure shipping the underlying is preferable for consumers.
Re: Artificial data give the same results as real data without compromising privacy
#24I'm highly dubious of the ability for synthetic data to model accurately datasets without introducing unexpected bias, esp. to account for causality. If you dig through the original paper, the conclusion is on the line with that: “For 7 out of 15 comparisons, we found no significant difference between the accuracy of features developed on the control dataset vs. those developed on some version of the synthesized data…
I'd like to read the paper before drawing such a conclusion. (the link to it seems to be broken)
"for 7 out of 15 comparisons, we found no significant difference" could mean all sorts of things. It could mean that 7 comparisons were perfect and 8 were complete garbage, as you suggest. Or it could mean that 7 comparisons were perfect and 8 had differences that were statistically significant, but the magnitudes of the differences were small enough that the results would still have been perfectly adequate for practical application.
In concrete terms: Let's say the synthetic data lets me build binary classifier that helps with a business issue, and has F1 scores of about 0.8. But if I had access to the real data, I could have got F1 of around 0.85. In that case, I'd happily take the data. As someone who's trying to solve business problems, it would be downright irresponsible of me to reject something that's better than what I currently have on the grounds that it's still less than some unattainable ideal.
Re: Artificial data give the same results as real data without compromising privacy
#25I'm highly dubious of the ability for synthetic data to model accurately datasets without introducing unexpected bias, esp. to account for causality. If you dig through the original paper, the conclusion is on the line with that: “For 7 out of 15 comparisons, we found no significant difference between the accuracy of features developed on the control dataset vs. those developed on some version of the synthesized data…
Haven't read the paper, but I will.
But I want to comment that it's worked for us. Sequence to sequence learning can reproduce every kind of iid and non-iid things we've ever looked at.
The real question is how safe/anonymous is it really?
Re: Artificial data give the same results as real data without compromising privacy
#26I'm highly dubious of the ability for synthetic data to model accurately datasets without introducing unexpected bias, esp. to account for causality. If you dig through the original paper, the conclusion is on the line with that: “For 7 out of 15 comparisons, we found no significant difference between the accuracy of features developed on the control dataset vs. those developed on some version of the synthesized data…
Agreed, seems suspect. If they are really able to learn the population-level distribution then why even bother generating fake data. Just release that instead.
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Re: Artificial data give the same results as real data without compromising privacy
#27I'm highly dubious of the ability for synthetic data to model accurately datasets without introducing unexpected bias, esp. to account for causality. If you dig through the original paper, the conclusion is on the line with that: “For 7 out of 15 comparisons, we found no significant difference between the accuracy of features developed on the control dataset vs. those developed on some version of the synthesized data…
Agreed, seems suspect. If they are really able to learn the population-level distribution then why even bother generating fake data. Just release that instead.
Well, just knowing a few distributions wouldn't be great for building machine learning models.
Re: Artificial data give the same results as real data without compromising privacy
#28I'm highly dubious of the ability for synthetic data to model accurately datasets without introducing unexpected bias, esp. to account for causality. If you dig through the original paper, the conclusion is on the line with that: “For 7 out of 15 comparisons, we found no significant difference between the accuracy of features developed on the control dataset vs. those developed on some version of the synthesized data…
Haven't read the paper, but I will. But I want to comment that it's worked for us. Sequence to sequence learning can reproduce every kind of iid and non-iid things we've ever looked at. The real question is how safe/anonymous is it really?
I imagine it depends on how closely you model the conditional probabilities.
If it gets down to correctly modeling the probability of colon cancer diagnosis by age, sex and ZIP code, and also the correct distribution of ages by ZIP code, then that'll be a potential problem in counties that only have one male 87-year-old.
Re: Artificial data give the same results as real data without compromising privacy
#29Earlier quoted context omitted.
The idea is to sidestep the need to access private information in order for researchers to do their work. So in this case, the artificial data is more useful, since the real data is inaccessible.
But the artificial data must come from somewhere? It can be modeled from real data in order to take into account outliers and to avoid cognitive biases in generation, but then there's still an initial reliance on the real data.
Yeah, the real data's properties are what are under study, so the artificial data needs to mimic it.
Re: Artificial data give the same results as real data without compromising privacy
#30Earlier quoted context omitted.
Haven't read the paper, but I will. But I want to comment that it's worked for us. Sequence to sequence learning can reproduce every kind of iid and non-iid things we've ever looked at. The real question is how safe/anonymous is it really?
I imagine it depends on how closely you model the conditional probabilities. If it gets down to correctly modeling the probability of colon cancer diagnosis by age, sex and ZIP code, and also the correct distribution of ages by ZIP code, then that'll be a potential problem in counties that only have one male 87-year-old.
I'm talking specifically about modeling iid/non-iid sequences of data from events, experiments, etc. Haven't read the paper so, I'm not sure if I'm talking past the authors or OP.