Differential privacy tools from MS Research and Harvard
blogs.microsoft.com
Differential privacy tools from MS Research and Harvard
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Re: Differential privacy tools from MS Research and Harvard
#2From https://github.com/opendifferentialprivacy/whitenoise-core:
"Differential privacy is the gold standard definition of privacy protection."
Differential privacy is nice, but it's still tracking and "less tracking" is absolutely not the gold standard definition of privacy protection. That title goes to "no tracking"
Re: Differential privacy tools from MS Research and Harvard
#3From https://github.com/opendifferentialprivacy/whitenoise-core : "Differential privacy is the gold standard definition of privacy protection." Differential privacy is nice, but it's still tracking and "less tracking" is absolutely not the gold standard definition of privacy protection. That title goes to "no tracking"
Privacy is not just relevant to advertising. There are a huge number of research opportunities in the social sciences that could benefit and can help make the world better.
Re: Differential privacy tools from MS Research and Harvard
#4From https://github.com/opendifferentialprivacy/whitenoise-core : "Differential privacy is the gold standard definition of privacy protection." Differential privacy is nice, but it's still tracking and "less tracking" is absolutely not the gold standard definition of privacy protection. That title goes to "no tracking"
Privacy is not just relevant to advertising. There are a huge number of research opportunities in the social sciences that could benefit and can help make the world better.
Show me what data you've collected and would like to share, including metadata, and if you ask nicely then most of the time I'd be more than happy to share it.
Things like emoji usage, page navigations, feature uses, etc. Ideally anonymous; no IP, user agent, etc, just a small byte or two packed properly can go a long way.
Re: Differential privacy tools from MS Research and Harvard
#5From https://github.com/opendifferentialprivacy/whitenoise-core : "Differential privacy is the gold standard definition of privacy protection." Differential privacy is nice, but it's still tracking and "less tracking" is absolutely not the gold standard definition of privacy protection. That title goes to "no tracking"
Privacy can't exist until we own software. We need property rights.
Re: Differential privacy tools from MS Research and Harvard
#6Earlier quoted context omitted.
Privacy is not just relevant to advertising. There are a huge number of research opportunities in the social sciences that could benefit and can help make the world better.
Show me what data you've collected and would like to share, including metadata, and if you ask nicely then most of the time I'd be more than happy to share it. Things like emoji usage, page navigations, feature uses, etc. Ideally anonymous; no IP, user agent, etc, just a small byte or two packed properly can go a long way.
>Ideally anonymous
The problem is that it's actually quite hard to reliably anonymize data especially once you start to begin combining data sets from multiple places. That's the problem differential privacy is trying to solve in a mathematically rigorous way.
See for example how researchers partially de-anonymized Netflix Prize data by cross-referencing it with IMDB reviews.
Re: Differential privacy tools from MS Research and Harvard
#7From https://github.com/opendifferentialprivacy/whitenoise-core : "Differential privacy is the gold standard definition of privacy protection." Differential privacy is nice, but it's still tracking and "less tracking" is absolutely not the gold standard definition of privacy protection. That title goes to "no tracking"
Privacy is not just relevant to advertising. There are a huge number of research opportunities in the social sciences that could benefit and can help make the world better.
Here's the fallacy: people think if they collect ALL the data, they will have the best results with whatever problem they're trying to solve.
Right now you're probably wondering, yeah, but there's this one problem that wouldn't have been solved if x or y....
But really it's just hoarding behavior. They're trying to collect it all. Statistical significance is reached very quickly and after that point they're doing harm to society.
Re: Differential privacy tools from MS Research and Harvard
#8From https://github.com/opendifferentialprivacy/whitenoise-core : "Differential privacy is the gold standard definition of privacy protection." Differential privacy is nice, but it's still tracking and "less tracking" is absolutely not the gold standard definition of privacy protection. That title goes to "no tracking"
> That title goes to "no tracking"
This is roughly analogous to "abstinence is the best form of birth control". It's not wrong, but it also isn't particularly realistic or helpful. The reality is that people often do want to exchange data, some times because it is legally or morally mandated, and tools that allow this to be done as safely as possible are important.
If you have free cycles, you can read more here:
https://github.com/frankmcsherry/blog/blob/master/posts/2017...
Re: Differential privacy tools from MS Research and Harvard
#9From https://github.com/opendifferentialprivacy/whitenoise-core : "Differential privacy is the gold standard definition of privacy protection." Differential privacy is nice, but it's still tracking and "less tracking" is absolutely not the gold standard definition of privacy protection. That title goes to "no tracking"
the focus of the project seems not to be on differential privacy as used by private companies but by scientists. in many cases you can't (US Census, medical records) or wouldn't want to (public health research) stop collecting the data. and allowing privacy-preserving statistical queries from outside researchers has the potential to be very useful.
Re: Differential privacy tools from MS Research and Harvard
#10Earlier quoted context omitted.
Show me what data you've collected and would like to share, including metadata, and if you ask nicely then most of the time I'd be more than happy to share it. Things like emoji usage, page navigations, feature uses, etc. Ideally anonymous; no IP, user agent, etc, just a small byte or two packed properly can go a long way.
>Ideally anonymous The problem is that it's actually quite hard to reliably anonymize data especially once you start to begin combining data sets from multiple places. That's the problem differential privacy is trying to solve in a mathematically rigorous way. See for example how researchers partially de-anonymized Netflix Prize data by cross-referencing it with IMDB reviews.
It's not possible to reliably anonymize data and still be able to infer something from it, the idea is just too logically broken. Because the whole reason for anonymity is to make sure no information about any individual or a set of individuals of the size decided by those seeking such protection can be inferred from the data by the parties that want to infer something from it. While "differential privacy" assumes that not being able to infer information about relatively small sized sets of individuals decided by the parties who want to infer something from the data is "privacy". It isn't of course, it's a pretty dystopian use of the word privacy. Hence why corporations love this stuff, privacy without privacy is godsent to them.