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Differential privacy tools from MS Research and Harvard

blogs.microsoft.com

11–20 of 23 posts

Re: Differential privacy tools from MS Research and Harvard

#11
post #2

From 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: htt…

I'm all for these tools being published and used where appropriate. I'm only criticizing their description as the "gold standard for privacy protection" which seems disingenuous.

Re: Differential privacy tools from MS Research and Harvard

#12
post #9
post #2

From 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.

> allowing privacy-preserving statistical queries from outside researchers has the potential to be very useful

This depends on how many researchers and queries per researcher that you want to allow. The privacy budget eventually runs out, so there are definitely drawbacks that prevent effective use of this data across enough outside researchers.

Re: Differential privacy tools from MS Research and Harvard

#13
post #6
post #4

Earlier 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.

Depends on the data.

DDG:

  { used_advanced_search }
  { used_country_toggle }
  { tabbed, *tab_maps }
  { filtered, *filter_date }
  { os "iOS", *ver "13.5", browser "Safari" }
iOS:

  Mail
  { disabled_remote_images }
  { flagged_mail }
  
  Keyboard
  { emoji_keyboard_via_globe }
  { *emoji_use "100-1000", *emojis [ ":)" ":P" ":(" ] }

Each of these could be stored separately without metadata then aggregated no problem. Things marked * could be left out, and some things could be randomized up or down buckets and such.

Re: Differential privacy tools from MS Research and Harvard

#14
> The amount of information revealed from each query is calculated and deducted from an overall privacy budget to halt additional queries

this is why DP doesn't get used in any real system -- limited # of searches is a deal breaker for any service that wants to monetize

there are some applications where this could be okay, like in-company surveys where you want to enable employees to run stats queries without revealing individuals, but companies are (relatively) high trust environments and DP is overkill

Re: Differential privacy tools from MS Research and Harvard

#16

> The amount of information revealed from each query is calculated and deducted from an overall privacy budget to halt additional queries this is why DP doesn't get used in any real system -- limited # of searches is a deal breaker for any service that wants to monetize there are some applications where this could be okay, like in-company surveys where you want to enable employees to run stats queries without reveali…

>this is why DP doesn't get used in any real system

It was used for the 2020 US Census.

There are some techniques like exposing randomized subsets for a limited number of queries.

Re: Differential privacy tools from MS Research and Harvard

#17
post #2

From 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: htt…

Abstinence only is an interesting meme. It's successful as a meme precisely because the advice does not work.

Re: Differential privacy tools from MS Research and Harvard

#18
post #16

> The amount of information revealed from each query is calculated and deducted from an overall privacy budget to halt additional queries this is why DP doesn't get used in any real system -- limited # of searches is a deal breaker for any service that wants to monetize there are some applications where this could be okay, like in-company surveys where you want to enable employees to run stats queries without reveali…

>this is why DP doesn't get used in any real system It was used for the 2020 US Census. There are some techniques like exposing randomized subsets for a limited number of queries.

!! didn't know that

how is access to the privacy prioritized?

Re: Differential privacy tools from MS Research and Harvard

#19
post #3
post #2

From 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.

Social scientists are hateful hateful people. They say they are like physicists but they are more similar to prosecutors.

"Everything you say can and will be used against you"

All they study is ways you are bad, and all they research is ways to keep you down.

If anyone reaches the wrong conclusion "maybe they are innocent.." Then it only takes two seconds, then they are fired.

Privacy from social scientists is one of the most important forms of privacy.

Re: Differential privacy tools from MS Research and Harvard

#20
post #16

Earlier quoted context omitted.

>this is why DP doesn't get used in any real system It was used for the 2020 US Census. There are some techniques like exposing randomized subsets for a limited number of queries.

!! didn't know that how is access to the privacy prioritized?

I haven't studied the Census' approach in detail but they have a fair bit of information online, e.g. https://www.census.gov/about/policies/privacy/statistical_sa...
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