
Show HN: Aggregate search DB for arbitration outcomes - awinter-py
https://github.com/abe-winter/arbout
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sirgawain33
I was curious how this application approached privacy budget management (e.g.
how the privacy parameter ε is accounted for over multiple searches), but,
flipping through the source, this application doesn't appear to use
Differential Privacy at all.

The anonymization approach implemented is "generalization". Here's a test
showing the outputs this app would produce:

[https://github.com/abe-
winter/arbout/blob/master/test/test_d...](https://github.com/abe-
winter/arbout/blob/master/test/test_diff_summary.py#L40)

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awinter-py
you're right, edited the title

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awinter-py
no wait, I'm not actually sure you're right

differential privacy is any search that protects individual inputs from
disclosure

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sirgawain33
Differential Privacy is a mathematical property. Here is the precise
definition:

[https://en.wikipedia.org/wiki/Differential_privacy#Definitio...](https://en.wikipedia.org/wiki/Differential_privacy#Definition_of_%CE%B5-differential_privacy)

One way to phrase it intuitively is "the probability of any particular output
from two databases that differ by one element is almost the same". The bound
on "almost" is captured by the privacy parameter ε.

One of the smoking guns that this algorithm is not differentially private is
that the code doesn't import `random` anywhere! A differentially private
algorithm is always going to be stochastic.

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awinter-py
I've removed refs to differential privacy from the repo -- I think you're
right and I misused the term

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leblancfg
I don't know enough about this to come up with search terms that give results.
Would OP / someone be able to provide example search terms? Cheers! :)

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theqult
i searched many queries leaving anything empty apart the state, nothing found.
Any working query ?

