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A group of ex-NSA and Amazon engineers are building a ‘GitHub for data’

420 points by malshe 7y ago

visarga 7y ago
It's not Github for data, but if it were, then one of the main issues is handling GDPR requests. Git is by construction averse to deletions in its history, so incompatible with hosting sensitive data. On the other hand it could be great to have deduplication in storage and versioning for datasets.
kick 7y ago
“We’re building right now software that enables developers to automatically check out an anonymized version of the data set,” said Watson. This so-called “synthetic data” is essentially artificial data that looks and works just like regular sensitive user data. Gretel uses machine learning to categorize the data — like names, addresses and other customer identifiers — and classify as many labels to the data as possible. Once that data is labeled, it can be applied access policies. Then, the platform applies differential privacy — a technique used to anonymize vast amounts of data — so that it’s no longer tied to customer information. Anyone taking bets on how long it's going to be before these idiots end up leaking the SSN of every US citizen because their categorizer failed?
- derision 7y ago
  How do you know they're idiots? Maybe you're the idiot
  - staticautomatic 7y ago
    You don't have to be a machine learning expert to understand that no classifier is going to be correct 100% of the time. The laws against divulging PII don't contain exceptions for classifiers goofing.
    - derision 7y ago
      And you don't have to present criticism by calling a group of people you don't know idiots.
    - thruflo 7y ago
      That’s not how it works. Synthetic data is entirely artificial rather than transformed, so you’re not worried about “missing some PII”. See for example the videos on https://hazy.com/product https://hazy.com/product Disclosure: Hazy cofounder.
      - staticautomatic 7y ago
        Why would you need "to anonymize vast amounts of data — so that it’s no longer tied to customer information" or "appl[y] access policies" if the data contain no PII? Presumably the ML is anonymizing the data and the access policies are necessary because the data contain PII.
        thruflo 7y ago
        Yup, people tend to confuse concepts and refer to synthetic data as anonymised data. They are very different things. Anonymised data or redacted data are transformations of a data set that _hopes_ not to leak too much PII / sensitive data. People don’t use ML to anonymise but they do use ML to classify as a first step before splatting or generalising. In that case, its absolutely right that the ML classifier not being 100% results in PII leaking. This is a key reason why anonymisation and redaction are widely seen as problematic and are being replaced by synthetic data and, maybe in future, homomorphic encryption.
        v4dok 7y ago
        Homomorphic encryption and any encryption in-use technology is no guarantee of privacy on its own. Synthetic data has the same dillema of utility vs anonymity as any other anonymization tech.
- infecto 7y ago
  Too negative my dude.
headcanon 7y ago
I'm a bit unclear on the goals for this startup. When you say "Github for Data" I'm thinking of a repository of datasets used for ML training or for more traditional research. But this: > This so-called “synthetic data” is essentially artificial data that looks and works just like regular sensitive user data. So its like Lorem Ipsum generator for data? Whats the use case here besides building apps with sample data? Notwithstanding potential privacy concerns, How am I confident that this is realistic if you literally say its generated? General data repository for research with some mechanism to ensure cleanliness or integrity sounds much more useful to me.
- ivalm 7y ago
  In health care AI there is some tendency to use generated data for training. Idea might be that org A has real patient data but for privacy reason cannot share data, but if they create a sufficiently strong generator they can share that and org B can train their classifier without ever accessing sensitive data. Alternatively, it can be also used if you simply have too little data and need augmentation.
  - ibarea277 7y ago
    This just seems like something that will catastrophically fail. If you can build a good enough generator you can just build the ML model internally. And if you can't the statistics of what you provide are going to be off enough that any strong model is going to be wrong in strange ways.
    - visarga 7y ago
      You can still pretrain on synthetic data and finetune on a smaller dataset of real data.
    - tomrod 7y ago
      Fraud modeling, regulatory requirements, economic data share (e.g. internal firm data) all represent potential use cases.
    - 7y ago
jayfk 7y ago
GitHub for sensitive data? Why would I trust an external company obfuscating my sensitive data and not my developers doing this for me?
- mirajshah 7y ago
  the same reason you trust external companies to do all sorts of other stuff for you: specialization. in the average case, they can probably do it better than your developers because it's their primary business.
  - jayfk 7y ago
    Sure, but we are talking about a twofold trust problem here. Trust in an entity (external company vs internal developer) and trust in the work itself. While I do see your point about trusting an external company which is specialized in the problem I’m trying to solve more than my own developers, I still have to transfer my highly sensitive data to them for which I have to trust them even more.
- jtm_tech 7y ago
  Great question. Frankly you shouldn't, and your developers should do it for you. While we'll be offering a service to enable this type of obfuscation via REST APIs (where we don't store or write any of your data to disk) we'll also be releasing some of these techniques as open source packages so your devs can kick the tires themselves without shipping data into someoneelse's service.
HashThis 7y ago
ex-NSA engineers. That has a negative brand to it. Especially and specifically when it comes to respect for data privacy.
- save_ferris 7y ago
  I’ve met several ex-NSAers that used their time there to build their brand successfully. At the end of the day, the dragnet surveillance decisions came from the highest levels of the Bush and Obama administrations, not the boots on the ground.
  - deleted 7y ago
    [deleted]
  - whatshisface 7y ago
    "I was just following orders," is not an excuse with a precedent of being accepted.
    - gruez 7y ago
      It works as long as the current regime isn't overthrown or your country gets occupied.
    - philosopher1234 7y ago
      Surveiling the population is not equivalent to genocide
      - Dahoon 7y ago
        No but it makes it easier if it comes to that.
    - sdinsn 7y ago
      No, the point is that the average NSA employee was not involved in dragnet at any capacity.
tompccs 7y ago
Since they're not launched yet, I think we can safely assume that this press release is not targeted at potential users.
travjones 7y ago
I think this is a great idea. Perhaps the "Github for data" copy could use some work, but the concept of obfuscating real data for use in building systems without the overhead or concern of operating on real customer data is valuable. Of course, the degree to which the obfuscated data represents real data is important to ensuring the systems built like this are robust, but this seems possible.
zitterbewegung 7y ago
I think a Gitlab for data is a much better proposition . See https://quiltdata.com/ https://quiltdata.com/ It is also open source and written in python. https://github.com/quiltdata/quilt https://github.com/quiltdata/quilt
freeone3000 7y ago
Why not use GitHub?
- sonicxxg 7y ago
  Well, Git itself is not a good tool for handling large dataset files. In most cases, you're not interested in deltas between commits. The size of your repo can also grow or of control pretty quickly. As a dirty workaround, you have Git-LFS to do that for you. People tend to use it in repos with a lot of multimedia assets. This works well in many cases, but it has its own pitfalls as well.
- alexwatson405 7y ago
  @freeone3000 GitHub is amazing for building and collaborating on source code, we're building services to enable safe and private collaboration on streaming data (whether text, images, video, your own format)
ivarv 7y ago
Wouldn't a data repository considered more of a wiki than vcs? I don't understand the desire to associate the concept with Github instead of Wikipedia, which I would propose is more appropriate.
- rednerrus 7y ago
  What if you wanted to work on a data set across groups and you wanted version control?
iblaine 7y ago
I see 'Github for data' but I'm reading services to hide PII. I think a distinction needs to be made here. Is the primary goal to enable Change Cata Capture on data(Github does CDC on code) or is the primary goal to manage PII?
- jtm_tech 7y ago
  Hey thanks for the great question. So the "Github for data" is referring to the ability to collaborate on data. By streaming in data, you can view discoveries we made on the data (entity recognition, etc) then essentially make a new version of that data with automatic transforms, anonymizations, etc. So you're absolutely right, managing PII is part of it, but really its about enabling entity A to share data with entity B with a high level of confidence the sensitive data is stripped out. We'll be releasing some of the packages to do the analysis and transformations as time goes on, so stay tuned for those so you can take them for a test drive yourself. Thanks!
wiremine 7y ago
A lot of the discussion here seems to be about the methodology and value of the "synthetic data" concept, and squaring that approach with the analogy to github. It feels like we can tease those two things apart: 1. Is a github-style website/service of forkable data sets useful? 2. Are anonymized, synthetic versions of those data sets, created via ML, useful? Feels like the answer to both is "yes"? (Also makes me wonder if there's a "rebase" equivalent for data in this sort of world...)
- justinclift 7y ago
  > 1. Is a github-style website/service of forkable data sets useful? Depends on the specifics, but it can be. I've been working on and off on getting a GitHub style data thing going (https://dbhub.io https://dbhub.io). It's still a work-in-progress, and I really need to get the data visualisation piece working, which is a pretty key feature and lousy to not have. ;)
m0zg 7y ago
There is already "GitHub for data": http://academictorrents.com/ http://academictorrents.com/
- oscarbatori 7y ago
  How does that even come close to solving the problems that a "GitHub for data" product would solve?
  - m0zg 7y ago
    Arguably, Academic Torrents is closer to what GitHub does than the product in question. I don't know why they compare it to GitHub TBH.
peteforde 7y ago
I started a company with the same premise 9 years ago, during the prime "big data" hype cycle. We burned through a similar amount of investor money only to realize that there was not a market opportunity to capture. That is, many people thought it was cool - we even did co-sponsored data contests with The Economist - but at the end of the day, we couldn't find anyone with an urgent problem that they were willing to pay to solve. I wish these folks luck! Perhaps things have changed; we were part of a flock of 5 or 10 similar projects and I'm pretty sure the only one still around today is Kaggle. https://www.youtube.com/watch?v=EWMjQhhxhQ4 https://www.youtube.com/watch?v=EWMjQhhxhQ4
- ritchiea 7y ago
  Yea this idea has been tried and never found a market at least a dozen times over. It's not impossible for the market or execution to change (e.g. Dropbox) but given the recent software is eating the world boom, it's hard to imagine that the market has changed dramatically so recently or their execution is going to be so much better than other teams who have tried and failed.
- reggieband 7y ago
  Sometimes an idea has a time. It may seem obvious now but it isn't like Instagram and Snapchat were the first image sharing applications developed. Slack was nowhere near the first chat app. I happened to have a long discussion on the topic of data businesses last night with a friend. We brainstormed datasets that would be a combination of hard/expensive to obtain while also having resell ability to thousands of customers who would be willing to pay a high value for them. I don't want to get involved in datasets that are easy to obtain (too many competitors, no bar to entry) or datasets specific to a particular company (too much dependence on a small set of customers, cost of acquiring new customers also includes cost of acquiring data, no economy of scale). It's easy to start with the tech problem: how to collect, clean and analyze data. But reasoning backwards from the business side is much more difficult. Expensive data I can sell once feels easy. Cheap data I can sell frequently feels like a race to the bottom. Expensive data I can re-sell 1000s of times to a niche audience feels like a perfect middle ground ... I just can't think of any examples.
symplee 7y ago
How about a GitHub for science?
- btrettel 7y ago
  GitHub itself works fine for science. I see no particularly compelling reason to use an science-oriented service given that people are more likely to be familiar with GitHub. Here's an example (or shameless plug): I use GitHub to share research data: https://github.com/btrettel/pipe-jet-breakup-data https://github.com/btrettel/pipe-jet-breakup-data There's code in that repository too. The code merges a variety of different data sources and performs some analyses. Nothing particularly fancy, and the code is probably not much better than average as far as academic code goes (which is not good), but I'm slowly adding tests and improving the code otherwise.
geraldbauer 7y ago
FYI: You might enjoy my talk notes titled "Using Git (and GitHub) for (Publishing) Data" [1] includes many real-world examples too. [1]: https://github.com/geraldb/talks/blob/master/git_for_data.md https://github.com/geraldb/talks/blob/master/git_for_data.md
rsashwin 7y ago
When I read the headlines, I thought it was these guys: https://www.dolthub.com https://www.dolthub.com
- tlbsofware 7y ago
  There’s a 404 at that link
  - oscarbatori 7y ago
    https://www.dolthub.com/docs/getting-started/introduction/ https://www.dolthub.com/docs/getting-started/introduction/ This is the correct link
- timsehn 7y ago
  It still cool when people know we exist. Thanks for the shout out. Git for data to us means a SQL database with Git semantics, backed by a content addressed graph of rows. That's what we built. Plus, we own http://www.gitfordata.com http://www.gitfordata.com, so we win.
lalaland1125 7y ago
When you hear a company talk about the promises of synthetic data, you should run far far away. The fundamental problem is that in order for synthetic data to be useful for model training the generative synthetic model must have already solved the problem at hand. Training on the synthetic data is just a charade: you would be better off simply extracting the model you need from the source generative model. For generative models with densities this is as simple as P(Y | X) = P(X, Y) \ P(X). So, what do you get out of it compared to simply training models as a service for people? Almost nothing useful. All you get are: - Much worse privacy guarantees. (People selling synthetic data love to talk about improved privacy, but it's actually the reverse. The privacy guarantees for synthetic data are much, much worse than selling direct models to people.) - Much worse model performance. See the previous notes about how a synthetic data generating procedure must have already solved the problem at hand. - A much more complicated setup with much more expensive model training. Training generative models is hard and requires a lot of data and compute due to the difficulty in learning such a complex outcome space. This can easily cost 100-1000x as much as simply training a straightforward xgboost model.
- adrianmonk 7y ago
  > must have already solved the problem at hand Assuming the problem at hand is analyzing the data. I don't know enough about what they're building to understand what use cases they have in mind. (Their blog says "developing new features or exploring insights", which is a bit vague. See https://medium.com/gretel-ai/gretel-readme-fd0c4eff8a09 https://medium.com/gretel-ai/gretel-readme-fd0c4eff8a09 .) But if this allows people to take their production database, run a command, and generate fake data they can run integration tests against, then that's not analyzing the data. It's making it easier to run tests in an environment that is as close as possible to production, because data is part of an environment. Aside from that, I'd argue that some kinds of analysis may even still be possible. Let's say you have an e-commerce site, with one database table for user accounts and another table for shipping addresses. And let's guess that Gretel anonymizes this data by creating a user table with different usernames and an address table with made-up shipping addresses, but the overall structure is isomorphic: for every row in real data, there is a corresponding row in fake data, just with different values in it. And the (synthetic) keys aren't private (they data you generated, not collected from the user), so they can be preserved. Then you can, on the fake data, run a query and find out what percentage of users have given a shipping address. Of course it's just a guess that that's what they're aiming to do since so little detail is available. It's entirely possible that what I described isn't what they're planning to do. Maybe it doesn't sufficiently protect privacy. But the point is, until we know what they're building, I don't know if we can conclude that people should run away.
dannykwells 7y ago
Honest question: does the NSA have good engineers? Like, FAANG good? It seems like a funny "hype line" for a startup, because I think of government engineers as ok, but not like, rockstars. But maybe I'm wrong?
- cashc 7y ago
  A single example, but Apache Accumulo came from NSA.
- dx87 7y ago
  Most of the top engineers would probably be contractors. The government pay scale pretty much forces you to become a manager if you want to move past GS-12. Including locality pay, GS-12 salary tops at a little over 100k a year[1]. I just looked at their website, and they don't mention anything about NSA, Amazon, or Google on the landing page, so I think it's just techcrunch adding it in as clickbait, even though the founders aren't going out of their way to advertise it. https://www.federalpay.org/gs/2020 https://www.federalpay.org/gs/2020
- wolco 7y ago
  The majority of the best engineers don't even work for a FAANG. FAANGs don't offer remote for the most part. How many of the best decided they wanted more. The world is a big place. NSA engineers on average would be scary good.
- jtm_tech 7y ago
  I think we're pretty good. Feel free to review the code and contribute as we release our open source packages over time. We all get better with time and collaboration.
michaelbuckbee 7y ago
I like this idea but the article doesn't do a great job of spitting out why it's valuable. Consider a SAAS with 25,000 active customers and tons of structured (database) data. You have a bunch of people that need to work on the dev system and the closer dev looks to prod the better you are. - Contractors in another country - A team working on basic compliance with GDPR and CCPA - Sysadmin team trying to manage backups/restores When the contractors pull a version of the DB it needs to not have any customer data (emails, addresses, etc.) so there's a process that wipes all those out and fills them in with fake data. When the GDPR team gets a data deletion request ("Please delete all my data my email is X@y.com") on Monday and the Sysadmin team restores from a backup from Sunday what happens? Right now both of these actions are one-off things done with a mish-mash of scripts, organizational knowledge, and half-remembered processes. So wouldn't this be better with a service that could talk to your DB and you could fork out versions that "knew" the current DB structure, that you could mark as purged of sensitive data, that you could apply (and re-apply) transforms to for structure to data addition/removal.
scarejunba 7y ago
Dolthub and Data.world sound more like that. This looks like one of numerous synthetic data startups.
that_girl 7y ago
I've been using Snowflake for a while now and the Data Sharing feature they have is weirdly close to what I'm reading here. Anonymization, masking a column etc. Anyone with similar experience as mine? Or anyone who's knowledgeable enough to compare this product with Snowflake for me?
gumby 7y ago
I don't want a github for data, I want a git for data. The one reason that POS Perforce is used in game development is that it can handle binary files, especially large ones. LFS is..OK. And this company isn't really even aping GitHub given that they are generating the content too.
- arxpoetica 7y ago
  What would you use a "git for data" for?
  - mhh__ 7y ago
    Data? Although having said then when you say data to me, I do imagine some level of immutability. Logging scientific results using git would be pretty good if required, in the sense that there is a habit to just use folders and text files which is fine at the time but is really really hard to take over sometimes (Like code written by scientists with their single-letter variable names and hatred of functions, in my experience of Fortran - yuck)
    - arxpoetica 7y ago
      I think I meant more in terms of use cases. So it sounds like one of your use cases is logging scientific results...?
    - DocSavage 7y ago
      We’ve released our branched versioning system for data at http://dvid.io http://dvid.io. It’s entirely open source and uses a science HTTP API with pluggable data types and a versioned key-value backend. I’m currently developing a new backend for it: DAGStore, a lower-level ordered key-value store that will have an explicit immutable store and is tailored for distributed versioning of big data.
  - gumby 7y ago
    Recording outputs of analytical runs or deep learning models; the ability to say ‘this is the output of program X v <git hash> run on the output of program Y <git hash> whose source data was Z <data-git hash>” for all the reasons of repeatability and audit ability needed for actual science.
JohnFen 7y ago
Engineers from the NSA, Google and Amazon worked on this? That doesn't exactly inspire any kind of confidence...
goofballlogic 7y ago
Great idea. I was just thinking about how this would be a great idea the other day.
quasiben 7y ago
Sounds very similar to the OSS DVC project: https://github.com/iterative/dvc https://github.com/iterative/dvc
deleted 7y ago
[deleted]
lettergram 7y ago
I work in this space, at the intersection of synthetic data and security: 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... The market opportunity is very large, but also insanely difficult to tap. For one, you have an uphill battle on trust. Customers have to trust your data is secure, and btw it’ll never be 100% secure by many standards. On the other hand, people have to trust the synthetic data is good enough to use Practically. So you have to both convince management and convince engineers. Arguably management is easier to convince, but.. best of luck on the endeavor! That’s not even discussing the technical challenges - I’ve implemented this all technically and have had it deployed to production systems. Building a robust system that is secure and produces valid synthetic data is a challenge.
amirouche 7y ago
It just happens that I have been working on something like that, posted on Show HN.
dpflan 7y ago
The article does not have a link to the company's website; this will save you a few actions: https://gretel.ai/ https://gretel.ai/
arxpoetica 7y ago
Assertion: GitHub for data can't exist without a Git for data. Naive take?
mhh__ 7y ago
Not entirely sure what this is yet but I was looking for a resource of scientific data in nice formats and I was very surprised I couldn't really find any. If this could do that in a way that doesn't upset any licensing agreements (Don't really know what I'm talking about in that regard) that would be pretty cool. I needed a Resitivity-Temperature dataset for a Tungsten alloy and I ended up having to manually type up a series out of the book, not fun!
xrd 7y ago
Is this the same Lazslo Bock, founder of Humu, formerly head of HR at Google? If so, very diverse set of founding experiences. If not, is this a common name in Hungary? Small world.
- Maxious 7y ago
  Same one, confirmed on his twitter https://twitter.com/LaszloBock/status/1230520252384993285 https://twitter.com/LaszloBock/status/1230520252384993285
sailfast 7y ago
"How do the CIA, NSA, DHS, and FBI all share critical data without breaking the law with regard to specific details about certain types of collection targets (like citizens?)"... FEDRAMP it. Now run that as a service inside a secure cloud environment. You need enough runway for the FEDRAMP / engineering / sales process plus a contract win and then I'd imagine the income gets pretty steady. Commercially? Unsure of the use case. I'd imagine as those sharing data are typically in competition. Not so in the government / intel community / finance space and I'd imagine you have to write a metric ton of policy and sign a bunch of MOUs to do this kind of stuff properly. People in government do care a great deal about these policies, believe it or not. This is also a huge problem to solve in the "Know Your Customer" / Anti-Money Laundering space for financial institutions, where sharing data between companies or government and companies is often prohibited and/or really difficult. See the recent FCA TechSprint for more on this: https://www.fca.org.uk/events/techsprints/aml-financial-crime-international-techsprint https://www.fca.org.uk/events/techsprints/aml-financial-crim... Lots of talk of "Homomorphic Encrpytion" and "Encrypted Cloud Runtimes" as options but if you don't really need to share all of the data to get to an outcome (but rather synthetic data - though synthetic identity seems to be the hard bit here...) that could be interesting!
TACIXAT 7y ago
I would like this as a marketplace where you could license your data to other people.
m463 7y ago
We already have: git lfs
dekhn 7y ago
I once took a call with an ex-NSA guy who was a CEO selling email security software- your MX would point to them, they'd scan your incoming email for exploits, then deliver it on to you. When I spoke to the guy, I expressed my concern, having working for a large multinational corporate whose fiber optic lines were tapped by UK intelligence to avoid NSA laws against spying on americans, that if I couldn't inspect his software, I couldn't feel confident of the security and integrity of the scanning system. I said, in good faith, that I would consider his product if I could inspect the running system and the code. He said several things: 1) the NSA never did anything illegal 2) the software was too large to audit 3) it was an insult to his employment in the NSA that I was even asking these questions. Then he hung up.
- bognition 7y ago
  I bet I can guess who this is
  - samstave 7y ago
    Why are we supposed to respect secrecy in this moment? Who is it?
    - arj111 7y ago
      Sounds like Area_1
- paulddraper 7y ago
  > the software was too large to audit That part's probably true. Try to audit something like OpenSSL.
  - rileymat2 7y ago
    Open ssl has be audited and fips validated for some versions
    - 7y ago
wildermuthn 7y ago
A little off-topic, since we’re not talking about true synthetic data here (vs. wiping PII), but the future of synthetic data is no-data due to differentiable programming. Instead of a program outputting vast amounts of synthetic data, it is written with a library or language that is differentiable and whose gradients can be integrated straight into the training of a model. A few PyTorch libraries dealing with 3D modeling have been released lately that accomplish that, and a good deal of work in Julia is making promising advances. I’m curious to see how overfitting will be addressed, but there may come a time when large datasets become a thing of the past as low-level generation of data becomes just another component of a model’s architecture.
notaharvardmba 7y ago
I haven't seen it mentioned yet so don't forget about the dat project: https://github.com/datproject/dat https://github.com/datproject/dat
opendomain 7y ago
I pitched exactly this years ago when I was the founder of NoSQL.com http://bit.ly/NoSQLPitchDeck http://bit.ly/NoSQLPitchDeck The problems with a 'Github for data' are the 7 'V's Volume: too much data to have usable diffs and merges Veracity: How do you know which branch of data to commit? Velocity: The data coming in is a stream - batch processing does not cut it Most companies end up creating a federation, not a true Data Mart.
__m 7y ago
Group of people with skewed moral compass are building a Github for data... yeah, not gonna push
v4dok 7y ago
Synthetic data is a tool in a system of privacy-preserving analytics. I don't think a lot of companies would be comfortable with shipping their sensitive data into a model sitting not on their premises. The model, needs to generate good enough results that would make sense in a training/analysis scenario (doubtful) and preserve the privacy of that data (difficult). People in the Privacy-preserving industry try to find the silver bullet in one technology, but the real solution is a combination of different technologies. Trusted delegation of computation + privacy-preserving techniques together solve this issue, but separately provide marginal value.
stazz1 7y ago
How about a version controlled law lookup, can I get an Amen?!
- Havoc 7y ago
  I'm sure I've seen this somewhere already. Don't recall what country though
luord 7y ago
That sounds like github in pretty much no way.
azernik 7y ago
Your detector is finely tuned - this is a user who routinely posts /r/conspiracy links in HN comments.
- samstave 7y ago
  I don't agree with you. However; My opinion is that you are a dick.
alpb 7y ago
I see "Laszlo Bock" is one of the founding members. He was industry veteran an exec at GM and Google (as a head of HR, as far as I know). He then started an HR company Humu (https://humu.com https://humu.com).
alexwatson405 7y ago
Hey all- we just released code on GitHub and a research post on Medium demonstrating how to generate synthetic datasets from models trained using differential privacy guarantees, based on rideshare datasets. Please take a look and let us know what you think! https://medium.com/gretel-ai/using-generative-differentially-private-models-to-build-privacy-enhancing-synthetic-datasets-c0633856184 https://medium.com/gretel-ai/using-generative-differentially...

420 points by malshe 7y ago

It's not Github for data, but if it were, then one of the main issues is handling GDPR requests. Git is by construction averse to deletions in its history, so incompatible with hosting sensitive data. On the other hand it could be great to have deduplication in storage and versioning for datasets.

kick 7y ago

“We’re building right now software that enables developers to automatically check out an anonymized version of the data set,” said Watson. This so-called “synthetic data” is essentially artificial data that looks and works just like regular sensitive user data. Gretel uses machine learning to categorize the data — like names, addresses and other customer identifiers — and classify as many labels to the data as possible. Once that data is labeled, it can be applied access policies. Then, the platform applies differential privacy — a technique used to anonymize vast amounts of data — so that it’s no longer tied to customer information. Anyone taking bets on how long it's going to be before these idiots end up leaking the SSN of every US citizen because their categorizer failed?

derision 7y ago
How do you know they're idiots? Maybe you're the idiot
- staticautomatic 7y ago
  You don't have to be a machine learning expert to understand that no classifier is going to be correct 100% of the time. The laws against divulging PII don't contain exceptions for classifiers goofing.
  - derision 7y ago
    And you don't have to present criticism by calling a group of people you don't know idiots.
  - thruflo 7y ago
    That’s not how it works. Synthetic data is entirely artificial rather than transformed, so you’re not worried about “missing some PII”. See for example the videos on https://hazy.com/product https://hazy.com/product Disclosure: Hazy cofounder.
    - staticautomatic 7y ago
      Why would you need "to anonymize vast amounts of data — so that it’s no longer tied to customer information" or "appl[y] access policies" if the data contain no PII? Presumably the ML is anonymizing the data and the access policies are necessary because the data contain PII.
      - thruflo 7y ago
        Yup, people tend to confuse concepts and refer to synthetic data as anonymised data. They are very different things. Anonymised data or redacted data are transformations of a data set that _hopes_ not to leak too much PII / sensitive data. People don’t use ML to anonymise but they do use ML to classify as a first step before splatting or generalising. In that case, its absolutely right that the ML classifier not being 100% results in PII leaking. This is a key reason why anonymisation and redaction are widely seen as problematic and are being replaced by synthetic data and, maybe in future, homomorphic encryption.
        v4dok 7y ago
        Homomorphic encryption and any encryption in-use technology is no guarantee of privacy on its own. Synthetic data has the same dillema of utility vs anonymity as any other anonymization tech.
infecto 7y ago
Too negative my dude.

headcanon 7y ago

I'm a bit unclear on the goals for this startup. When you say "Github for Data" I'm thinking of a repository of datasets used for ML training or for more traditional research. But this: > This so-called “synthetic data” is essentially artificial data that looks and works just like regular sensitive user data. So its like Lorem Ipsum generator for data? Whats the use case here besides building apps with sample data? Notwithstanding potential privacy concerns, How am I confident that this is realistic if you literally say its generated? General data repository for research with some mechanism to ensure cleanliness or integrity sounds much more useful to me.

ivalm 7y ago
In health care AI there is some tendency to use generated data for training. Idea might be that org A has real patient data but for privacy reason cannot share data, but if they create a sufficiently strong generator they can share that and org B can train their classifier without ever accessing sensitive data. Alternatively, it can be also used if you simply have too little data and need augmentation.
- ibarea277 7y ago
  This just seems like something that will catastrophically fail. If you can build a good enough generator you can just build the ML model internally. And if you can't the statistics of what you provide are going to be off enough that any strong model is going to be wrong in strange ways.
  - visarga 7y ago
    You can still pretrain on synthetic data and finetune on a smaller dataset of real data.
  - tomrod 7y ago
    Fraud modeling, regulatory requirements, economic data share (e.g. internal firm data) all represent potential use cases.
  - 7y ago

jayfk 7y ago

GitHub for sensitive data? Why would I trust an external company obfuscating my sensitive data and not my developers doing this for me?

mirajshah 7y ago
the same reason you trust external companies to do all sorts of other stuff for you: specialization. in the average case, they can probably do it better than your developers because it's their primary business.
- jayfk 7y ago
  Sure, but we are talking about a twofold trust problem here. Trust in an entity (external company vs internal developer) and trust in the work itself. While I do see your point about trusting an external company which is specialized in the problem I’m trying to solve more than my own developers, I still have to transfer my highly sensitive data to them for which I have to trust them even more.
jtm_tech 7y ago
Great question. Frankly you shouldn't, and your developers should do it for you. While we'll be offering a service to enable this type of obfuscation via REST APIs (where we don't store or write any of your data to disk) we'll also be releasing some of these techniques as open source packages so your devs can kick the tires themselves without shipping data into someoneelse's service.

HashThis 7y ago

ex-NSA engineers. That has a negative brand to it. Especially and specifically when it comes to respect for data privacy.

save_ferris 7y ago
I’ve met several ex-NSAers that used their time there to build their brand successfully. At the end of the day, the dragnet surveillance decisions came from the highest levels of the Bush and Obama administrations, not the boots on the ground.
- deleted 7y ago
  [deleted]
- whatshisface 7y ago
  "I was just following orders," is not an excuse with a precedent of being accepted.
  - gruez 7y ago
    It works as long as the current regime isn't overthrown or your country gets occupied.
  - philosopher1234 7y ago
    Surveiling the population is not equivalent to genocide
    - Dahoon 7y ago
      No but it makes it easier if it comes to that.
  - sdinsn 7y ago
    No, the point is that the average NSA employee was not involved in dragnet at any capacity.

tompccs 7y ago

Since they're not launched yet, I think we can safely assume that this press release is not targeted at potential users.

travjones 7y ago

I think this is a great idea. Perhaps the "Github for data" copy could use some work, but the concept of obfuscating real data for use in building systems without the overhead or concern of operating on real customer data is valuable. Of course, the degree to which the obfuscated data represents real data is important to ensuring the systems built like this are robust, but this seems possible.

zitterbewegung 7y ago

I think a Gitlab for data is a much better proposition . See https://quiltdata.com/ https://quiltdata.com/ It is also open source and written in python. https://github.com/quiltdata/quilt https://github.com/quiltdata/quilt

freeone3000 7y ago

Why not use GitHub?

sonicxxg 7y ago
Well, Git itself is not a good tool for handling large dataset files. In most cases, you're not interested in deltas between commits. The size of your repo can also grow or of control pretty quickly. As a dirty workaround, you have Git-LFS to do that for you. People tend to use it in repos with a lot of multimedia assets. This works well in many cases, but it has its own pitfalls as well.
alexwatson405 7y ago
@freeone3000 GitHub is amazing for building and collaborating on source code, we're building services to enable safe and private collaboration on streaming data (whether text, images, video, your own format)

ivarv 7y ago

Wouldn't a data repository considered more of a wiki than vcs? I don't understand the desire to associate the concept with Github instead of Wikipedia, which I would propose is more appropriate.

rednerrus 7y ago
What if you wanted to work on a data set across groups and you wanted version control?

iblaine 7y ago

I see 'Github for data' but I'm reading services to hide PII. I think a distinction needs to be made here. Is the primary goal to enable Change Cata Capture on data(Github does CDC on code) or is the primary goal to manage PII?

jtm_tech 7y ago
Hey thanks for the great question. So the "Github for data" is referring to the ability to collaborate on data. By streaming in data, you can view discoveries we made on the data (entity recognition, etc) then essentially make a new version of that data with automatic transforms, anonymizations, etc. So you're absolutely right, managing PII is part of it, but really its about enabling entity A to share data with entity B with a high level of confidence the sensitive data is stripped out. We'll be releasing some of the packages to do the analysis and transformations as time goes on, so stay tuned for those so you can take them for a test drive yourself. Thanks!

wiremine 7y ago

A lot of the discussion here seems to be about the methodology and value of the "synthetic data" concept, and squaring that approach with the analogy to github. It feels like we can tease those two things apart: 1. Is a github-style website/service of forkable data sets useful? 2. Are anonymized, synthetic versions of those data sets, created via ML, useful? Feels like the answer to both is "yes"? (Also makes me wonder if there's a "rebase" equivalent for data in this sort of world...)

justinclift 7y ago
> 1. Is a github-style website/service of forkable data sets useful? Depends on the specifics, but it can be. I've been working on and off on getting a GitHub style data thing going (https://dbhub.io https://dbhub.io). It's still a work-in-progress, and I really need to get the data visualisation piece working, which is a pretty key feature and lousy to not have. ;)

m0zg 7y ago

There is already "GitHub for data": http://academictorrents.com/ http://academictorrents.com/

oscarbatori 7y ago
How does that even come close to solving the problems that a "GitHub for data" product would solve?
- m0zg 7y ago
  Arguably, Academic Torrents is closer to what GitHub does than the product in question. I don't know why they compare it to GitHub TBH.

peteforde 7y ago

I started a company with the same premise 9 years ago, during the prime "big data" hype cycle. We burned through a similar amount of investor money only to realize that there was not a market opportunity to capture. That is, many people thought it was cool - we even did co-sponsored data contests with The Economist - but at the end of the day, we couldn't find anyone with an urgent problem that they were willing to pay to solve. I wish these folks luck! Perhaps things have changed; we were part of a flock of 5 or 10 similar projects and I'm pretty sure the only one still around today is Kaggle. https://www.youtube.com/watch?v=EWMjQhhxhQ4 https://www.youtube.com/watch?v=EWMjQhhxhQ4

ritchiea 7y ago
Yea this idea has been tried and never found a market at least a dozen times over. It's not impossible for the market or execution to change (e.g. Dropbox) but given the recent software is eating the world boom, it's hard to imagine that the market has changed dramatically so recently or their execution is going to be so much better than other teams who have tried and failed.
reggieband 7y ago
Sometimes an idea has a time. It may seem obvious now but it isn't like Instagram and Snapchat were the first image sharing applications developed. Slack was nowhere near the first chat app. I happened to have a long discussion on the topic of data businesses last night with a friend. We brainstormed datasets that would be a combination of hard/expensive to obtain while also having resell ability to thousands of customers who would be willing to pay a high value for them. I don't want to get involved in datasets that are easy to obtain (too many competitors, no bar to entry) or datasets specific to a particular company (too much dependence on a small set of customers, cost of acquiring new customers also includes cost of acquiring data, no economy of scale). It's easy to start with the tech problem: how to collect, clean and analyze data. But reasoning backwards from the business side is much more difficult. Expensive data I can sell once feels easy. Cheap data I can sell frequently feels like a race to the bottom. Expensive data I can re-sell 1000s of times to a niche audience feels like a perfect middle ground ... I just can't think of any examples.

symplee 7y ago

How about a GitHub for science?

btrettel 7y ago
GitHub itself works fine for science. I see no particularly compelling reason to use an science-oriented service given that people are more likely to be familiar with GitHub. Here's an example (or shameless plug): I use GitHub to share research data: https://github.com/btrettel/pipe-jet-breakup-data https://github.com/btrettel/pipe-jet-breakup-data There's code in that repository too. The code merges a variety of different data sources and performs some analyses. Nothing particularly fancy, and the code is probably not much better than average as far as academic code goes (which is not good), but I'm slowly adding tests and improving the code otherwise.

geraldbauer 7y ago

FYI: You might enjoy my talk notes titled "Using Git (and GitHub) for (Publishing) Data" [1] includes many real-world examples too. [1]: https://github.com/geraldb/talks/blob/master/git_for_data.md https://github.com/geraldb/talks/blob/master/git_for_data.md

rsashwin 7y ago

When I read the headlines, I thought it was these guys: https://www.dolthub.com https://www.dolthub.com

tlbsofware 7y ago
There’s a 404 at that link
- oscarbatori 7y ago
  https://www.dolthub.com/docs/getting-started/introduction/ https://www.dolthub.com/docs/getting-started/introduction/ This is the correct link
timsehn 7y ago
It still cool when people know we exist. Thanks for the shout out. Git for data to us means a SQL database with Git semantics, backed by a content addressed graph of rows. That's what we built. Plus, we own http://www.gitfordata.com http://www.gitfordata.com, so we win.

lalaland1125 7y ago

When you hear a company talk about the promises of synthetic data, you should run far far away. The fundamental problem is that in order for synthetic data to be useful for model training the generative synthetic model must have already solved the problem at hand. Training on the synthetic data is just a charade: you would be better off simply extracting the model you need from the source generative model. For generative models with densities this is as simple as P(Y | X) = P(X, Y) \ P(X). So, what do you get out of it compared to simply training models as a service for people? Almost nothing useful. All you get are: - Much worse privacy guarantees. (People selling synthetic data love to talk about improved privacy, but it's actually the reverse. The privacy guarantees for synthetic data are much, much worse than selling direct models to people.) - Much worse model performance. See the previous notes about how a synthetic data generating procedure must have already solved the problem at hand. - A much more complicated setup with much more expensive model training. Training generative models is hard and requires a lot of data and compute due to the difficulty in learning such a complex outcome space. This can easily cost 100-1000x as much as simply training a straightforward xgboost model.

adrianmonk 7y ago
> must have already solved the problem at hand Assuming the problem at hand is analyzing the data. I don't know enough about what they're building to understand what use cases they have in mind. (Their blog says "developing new features or exploring insights", which is a bit vague. See https://medium.com/gretel-ai/gretel-readme-fd0c4eff8a09 https://medium.com/gretel-ai/gretel-readme-fd0c4eff8a09 .) But if this allows people to take their production database, run a command, and generate fake data they can run integration tests against, then that's not analyzing the data. It's making it easier to run tests in an environment that is as close as possible to production, because data is part of an environment. Aside from that, I'd argue that some kinds of analysis may even still be possible. Let's say you have an e-commerce site, with one database table for user accounts and another table for shipping addresses. And let's guess that Gretel anonymizes this data by creating a user table with different usernames and an address table with made-up shipping addresses, but the overall structure is isomorphic: for every row in real data, there is a corresponding row in fake data, just with different values in it. And the (synthetic) keys aren't private (they data you generated, not collected from the user), so they can be preserved. Then you can, on the fake data, run a query and find out what percentage of users have given a shipping address. Of course it's just a guess that that's what they're aiming to do since so little detail is available. It's entirely possible that what I described isn't what they're planning to do. Maybe it doesn't sufficiently protect privacy. But the point is, until we know what they're building, I don't know if we can conclude that people should run away.

dannykwells 7y ago

Honest question: does the NSA have good engineers? Like, FAANG good? It seems like a funny "hype line" for a startup, because I think of government engineers as ok, but not like, rockstars. But maybe I'm wrong?

cashc 7y ago
A single example, but Apache Accumulo came from NSA.
dx87 7y ago
Most of the top engineers would probably be contractors. The government pay scale pretty much forces you to become a manager if you want to move past GS-12. Including locality pay, GS-12 salary tops at a little over 100k a year[1]. I just looked at their website, and they don't mention anything about NSA, Amazon, or Google on the landing page, so I think it's just techcrunch adding it in as clickbait, even though the founders aren't going out of their way to advertise it. https://www.federalpay.org/gs/2020 https://www.federalpay.org/gs/2020
wolco 7y ago
The majority of the best engineers don't even work for a FAANG. FAANGs don't offer remote for the most part. How many of the best decided they wanted more. The world is a big place. NSA engineers on average would be scary good.
jtm_tech 7y ago
I think we're pretty good. Feel free to review the code and contribute as we release our open source packages over time. We all get better with time and collaboration.

michaelbuckbee 7y ago

I like this idea but the article doesn't do a great job of spitting out why it's valuable. Consider a SAAS with 25,000 active customers and tons of structured (database) data. You have a bunch of people that need to work on the dev system and the closer dev looks to prod the better you are. - Contractors in another country - A team working on basic compliance with GDPR and CCPA - Sysadmin team trying to manage backups/restores When the contractors pull a version of the DB it needs to not have any customer data (emails, addresses, etc.) so there's a process that wipes all those out and fills them in with fake data. When the GDPR team gets a data deletion request ("Please delete all my data my email is X@y.com") on Monday and the Sysadmin team restores from a backup from Sunday what happens? Right now both of these actions are one-off things done with a mish-mash of scripts, organizational knowledge, and half-remembered processes. So wouldn't this be better with a service that could talk to your DB and you could fork out versions that "knew" the current DB structure, that you could mark as purged of sensitive data, that you could apply (and re-apply) transforms to for structure to data addition/removal.

scarejunba 7y ago

Dolthub and Data.world sound more like that. This looks like one of numerous synthetic data startups.

that_girl 7y ago

I've been using Snowflake for a while now and the Data Sharing feature they have is weirdly close to what I'm reading here. Anonymization, masking a column etc. Anyone with similar experience as mine? Or anyone who's knowledgeable enough to compare this product with Snowflake for me?

gumby 7y ago

I don't want a github for data, I want a git for data. The one reason that POS Perforce is used in game development is that it can handle binary files, especially large ones. LFS is..OK. And this company isn't really even aping GitHub given that they are generating the content too.

arxpoetica 7y ago
What would you use a "git for data" for?
- mhh__ 7y ago
  Data? Although having said then when you say data to me, I do imagine some level of immutability. Logging scientific results using git would be pretty good if required, in the sense that there is a habit to just use folders and text files which is fine at the time but is really really hard to take over sometimes (Like code written by scientists with their single-letter variable names and hatred of functions, in my experience of Fortran - yuck)
  - arxpoetica 7y ago
    I think I meant more in terms of use cases. So it sounds like one of your use cases is logging scientific results...?
  - DocSavage 7y ago
    We’ve released our branched versioning system for data at http://dvid.io http://dvid.io. It’s entirely open source and uses a science HTTP API with pluggable data types and a versioned key-value backend. I’m currently developing a new backend for it: DAGStore, a lower-level ordered key-value store that will have an explicit immutable store and is tailored for distributed versioning of big data.
- gumby 7y ago
  Recording outputs of analytical runs or deep learning models; the ability to say ‘this is the output of program X v <git hash> run on the output of program Y <git hash> whose source data was Z <data-git hash>” for all the reasons of repeatability and audit ability needed for actual science.

JohnFen 7y ago

Engineers from the NSA, Google and Amazon worked on this? That doesn't exactly inspire any kind of confidence...

goofballlogic 7y ago

Great idea. I was just thinking about how this would be a great idea the other day.

quasiben 7y ago

Sounds very similar to the OSS DVC project: https://github.com/iterative/dvc https://github.com/iterative/dvc

deleted 7y ago

[deleted]

lettergram 7y ago

I work in this space, at the intersection of synthetic data and security: 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... The market opportunity is very large, but also insanely difficult to tap. For one, you have an uphill battle on trust. Customers have to trust your data is secure, and btw it’ll never be 100% secure by many standards. On the other hand, people have to trust the synthetic data is good enough to use Practically. So you have to both convince management and convince engineers. Arguably management is easier to convince, but.. best of luck on the endeavor! That’s not even discussing the technical challenges - I’ve implemented this all technically and have had it deployed to production systems. Building a robust system that is secure and produces valid synthetic data is a challenge.

amirouche 7y ago

It just happens that I have been working on something like that, posted on Show HN.

dpflan 7y ago

The article does not have a link to the company's website; this will save you a few actions: https://gretel.ai/ https://gretel.ai/

arxpoetica 7y ago

Assertion: GitHub for data can't exist without a Git for data. Naive take?

mhh__ 7y ago

Not entirely sure what this is yet but I was looking for a resource of scientific data in nice formats and I was very surprised I couldn't really find any. If this could do that in a way that doesn't upset any licensing agreements (Don't really know what I'm talking about in that regard) that would be pretty cool. I needed a Resitivity-Temperature dataset for a Tungsten alloy and I ended up having to manually type up a series out of the book, not fun!

xrd 7y ago

Is this the same Lazslo Bock, founder of Humu, formerly head of HR at Google? If so, very diverse set of founding experiences. If not, is this a common name in Hungary? Small world.

Maxious 7y ago
Same one, confirmed on his twitter https://twitter.com/LaszloBock/status/1230520252384993285 https://twitter.com/LaszloBock/status/1230520252384993285

sailfast 7y ago

"How do the CIA, NSA, DHS, and FBI all share critical data without breaking the law with regard to specific details about certain types of collection targets (like citizens?)"... FEDRAMP it. Now run that as a service inside a secure cloud environment. You need enough runway for the FEDRAMP / engineering / sales process plus a contract win and then I'd imagine the income gets pretty steady. Commercially? Unsure of the use case. I'd imagine as those sharing data are typically in competition. Not so in the government / intel community / finance space and I'd imagine you have to write a metric ton of policy and sign a bunch of MOUs to do this kind of stuff properly. People in government do care a great deal about these policies, believe it or not. This is also a huge problem to solve in the "Know Your Customer" / Anti-Money Laundering space for financial institutions, where sharing data between companies or government and companies is often prohibited and/or really difficult. See the recent FCA TechSprint for more on this: https://www.fca.org.uk/events/techsprints/aml-financial-crime-international-techsprint https://www.fca.org.uk/events/techsprints/aml-financial-crim... Lots of talk of "Homomorphic Encrpytion" and "Encrypted Cloud Runtimes" as options but if you don't really need to share all of the data to get to an outcome (but rather synthetic data - though synthetic identity seems to be the hard bit here...) that could be interesting!

TACIXAT 7y ago

I would like this as a marketplace where you could license your data to other people.

m463 7y ago

We already have: git lfs

dekhn 7y ago

I once took a call with an ex-NSA guy who was a CEO selling email security software- your MX would point to them, they'd scan your incoming email for exploits, then deliver it on to you. When I spoke to the guy, I expressed my concern, having working for a large multinational corporate whose fiber optic lines were tapped by UK intelligence to avoid NSA laws against spying on americans, that if I couldn't inspect his software, I couldn't feel confident of the security and integrity of the scanning system. I said, in good faith, that I would consider his product if I could inspect the running system and the code. He said several things: 1) the NSA never did anything illegal 2) the software was too large to audit 3) it was an insult to his employment in the NSA that I was even asking these questions. Then he hung up.

bognition 7y ago
I bet I can guess who this is
- samstave 7y ago
  Why are we supposed to respect secrecy in this moment? Who is it?
  - arj111 7y ago
    Sounds like Area_1
paulddraper 7y ago
> the software was too large to audit That part's probably true. Try to audit something like OpenSSL.
- rileymat2 7y ago
  Open ssl has be audited and fips validated for some versions
  - 7y ago

wildermuthn 7y ago

A little off-topic, since we’re not talking about true synthetic data here (vs. wiping PII), but the future of synthetic data is no-data due to differentiable programming. Instead of a program outputting vast amounts of synthetic data, it is written with a library or language that is differentiable and whose gradients can be integrated straight into the training of a model. A few PyTorch libraries dealing with 3D modeling have been released lately that accomplish that, and a good deal of work in Julia is making promising advances. I’m curious to see how overfitting will be addressed, but there may come a time when large datasets become a thing of the past as low-level generation of data becomes just another component of a model’s architecture.

notaharvardmba 7y ago

I haven't seen it mentioned yet so don't forget about the dat project: https://github.com/datproject/dat https://github.com/datproject/dat

opendomain 7y ago

I pitched exactly this years ago when I was the founder of NoSQL.com http://bit.ly/NoSQLPitchDeck http://bit.ly/NoSQLPitchDeck The problems with a 'Github for data' are the 7 'V's Volume: too much data to have usable diffs and merges Veracity: How do you know which branch of data to commit? Velocity: The data coming in is a stream - batch processing does not cut it Most companies end up creating a federation, not a true Data Mart.

__m 7y ago

Group of people with skewed moral compass are building a Github for data... yeah, not gonna push

v4dok 7y ago

Synthetic data is a tool in a system of privacy-preserving analytics. I don't think a lot of companies would be comfortable with shipping their sensitive data into a model sitting not on their premises. The model, needs to generate good enough results that would make sense in a training/analysis scenario (doubtful) and preserve the privacy of that data (difficult). People in the Privacy-preserving industry try to find the silver bullet in one technology, but the real solution is a combination of different technologies. Trusted delegation of computation + privacy-preserving techniques together solve this issue, but separately provide marginal value.

stazz1 7y ago

How about a version controlled law lookup, can I get an Amen?!

Havoc 7y ago
I'm sure I've seen this somewhere already. Don't recall what country though

luord 7y ago

That sounds like github in pretty much no way.

azernik 7y ago

Your detector is finely tuned - this is a user who routinely posts /r/conspiracy links in HN comments.

samstave 7y ago
I don't agree with you. However; My opinion is that you are a dick.

alpb 7y ago

I see "Laszlo Bock" is one of the founding members. He was industry veteran an exec at GM and Google (as a head of HR, as far as I know). He then started an HR company Humu (https://humu.com https://humu.com).

alexwatson405 7y ago

Hey all- we just released code on GitHub and a research post on Medium demonstrating how to generate synthetic datasets from models trained using differential privacy guarantees, based on rideshare datasets. Please take a look and let us know what you think! https://medium.com/gretel-ai/using-generative-differentially-private-models-to-build-privacy-enhancing-synthetic-datasets-c0633856184 https://medium.com/gretel-ai/using-generative-differentially...