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Rendered at 08:13:55 GMT+0000 (Coordinated Universal Time) with Cloudflare Workers.
All 500,000 participants for sale on Alibaba...
And official response: https://www.ukbiobank.ac.uk/news/a-message-to-our-participan...
"The charity did not specify the types of data that were included, but Murray stated in the Commons that several markers were included in the listings:
- Gender
- Age
- Month and year of birth
- Assessment center data
- Attendance dates
- Socioeconomic status
- Lifestyle habits
- Measures from biological samples related to haematology, biology, and chemistry
- Sleep, diet, work environment, mental health, and health outcomes data."
the field of de-anonymization is booming.
[0] https://www.bennett.ox.ac.uk/blog/2025/02/opensafely-in-brie...
I'm also amused (in a good way) by the fact that SAS isn't supported as an analysis platform...
On the other hand I like opensafely's approach to security: no individual data is ever shared with researchers.
And some information on how they were distributing it to researchers: https://github.com/broadinstitute/ml4h/blob/master/ingest/uk...
> The following steps require the ukbunpack and ukbconv utilities from the UK Biobank website. The file decrypt_all.sh will run through the following steps on one of the on-prem servers.
> Once the data is downloaded, it needs to be "ukbunpacked" which decrypts it, and then converts it to a file format of choice. Both ukbunpack and ukbconv are available from the UK Biobank's website. The decryption has to happen on a linux system if you download the linux tools, e.g. the Broad's on-prem servers. Note that you need plenty of space to decrypt/unpack, and the programs may fail silently if disk space runs out during the middle.
I am aware of ~30 repositories that UK Biobank has asked GitHub to delete, and can still be found elsewhere online. They know the site, they have managed to delete data from that site before, and yet the files are still there.
(The first is a GitHub repo for https://www.weizmann.ac.il/math/tanay/home )
If an 'anonymised' medical record says the person was born 6th September 1969, received treatment for a broken arm on 1 April 2004, and received a course of treatment in 2009 after catching the clap on holiday in Thailand - that's enough bits of information to uniquely identify me.
And medical researchers are usually very big on 'fully informed consent' so they can't gloss over that reality, hide it in fine print or obsfucate it with flowerly language. They usually have to make sure the participants really understand what they're agreeing to.
It might still work out fine, of course - 95% of people's medical histories don't contain anything particularly embarrassing, so you might be able to get plenty of participants anyway.
Unfortunately the sequence of treatments and locations are usually enough to identify someone, especially if it's a rarer condition.
Most researchers likely would want to summarize these data in a similar way anyway, so this works out nicely.
Yeah, sorry about that
If you want such a project you need to have a new project with a different agreement. I doubt you could get as many volunteers to freely give away such intimate data to anyone who wants though
But what this illustrates to me is that researchers are just really careless, despite everything we make them agree to in data transfer agreements. It seems absurd to have little cubicles like this https://safepodnetwork.ac.uk/ (think Mission Impossible 1) but I do despair.
In the EU, there is a bigger interest in building scalable but also secure platforms for health data. Hopefully good innovation will come from there.
It looks like they've identified the institutions, at least... but aren't identifying it to the public for now. Are there going to be consequences? Are they going to be identified and sanctioned beyond "having their access suspended?"
In the US, HHS wouldn't hestitate to name, shame, and impose a sanction with corrective action plans. Not knowing much about how things work across the pond, I'm sure CMS PII gets used more often in research without these leaks left and right.
Enforcing Jupytext is a good adaption, and gives you all the, arguably really nice, comfort from a notebook, and the proper code practice from SW engineering.
To me it seems rather naive to have done that.
After all, you can't un-leak medical data. So even if the "strict agreement" included huge punishments, there's no getting the toothpaste back in the tube.
If you want to ensure compliance before a leak happens you have to (ugh) audit their compliance. And that isn't something that scales to 20,000 researchers.
Too late to do anything about it now though :(
Then there's the question of trust. You probably have friends you know not to tell certain secrets to, because they believe they get to delegate your secrets onwards to people they trust. The further away someone is from you, the less respect they will show. Researchers have been loaning the dataset in good faith to people who they trust, but who probably didn't take the whole secrecy thing as seriously.
With 20k researchers this was inevitable. The kind of factors above need to be factored in when designing on what grounds such a dataset is to be released.
Internalizing that people are frequently less intelligent and less respectful and less kind and less thoughtful than you previously believed is a tragic loss of innocence and hope, not a favorable experience.
And in the end it is designed _by_ idiots and only idiots find it usable.
Reckless harm prevention is the root of many evils.
Sensitive research systems thread that needle by giving remote access to researchers with the data in the control and supervision of the responsible organization. Strong internal data access controls and data siloing alongside strict verified extraction routines. Specifically: limited project-dedicated DB access, full logging of data interactions, and full lockouts/freezes if something feels off.
‘The five safes’ is a good presentation from the NHS(?) a decade ago covering the approaches.
Data publishing restrictions around health data aren’t reckless. Modern computing and digital permanence mean we have to be extra cautious.
Any friction you add to "access the data" process makes it harder for legitimate researchers to get access to, and get benefits from that data.
So, at what point do stricter data controls begin to choke you at the throat?
Perhaps our future ai overlords will feel its important to compartmentalise, and log data access more agressively.
That being said, I may be as pessmistic as you are: I don't think people right now grasp how standards for deidentification may no longer be enough, and how easy and automated deanonymization changes everything. Unfortunately, cuts to federal science agencies means that I doubt any well-informed guidance will come soon.