Eventually, no matter how virtuous a company claims to be, they'll sell the company to FAANG who I try to avoid as much as possible. When the true end goal of most startups is an exit payday to Google I can't trust them with my data.
It's frustrating that avoiding Google isn't even as simple as "Don't use Google". It's become "Don't use anyone who might be an acquisition target for Google".
And their appealing product will probably get shut down or rendered unrecognizable, either by FAANG or by bankruptcy. I've pretty much sworn off all startups that sell services, products dependent on services, or anything that requires "the cloud."
At some point, my data's going to be sold off and monetized, and I won't even be able to enjoy the product I was supposed to get in return.
It took just two years to go from rejecting the FBI's request for one user's keys, to handing over the encryption keys to China for millions of users.
Apple was asked by China to move the user data and it's keystore to local Guizhou-Cloud data centers. Apple updated their TOS and blocked service to anyone who didn't agree to moving their user data. [1]
These datacenters were nationalized just several months ago [2]:
> Fast forward to today: China Telecom, a government owned telco, is taking over the iCloud data from Guizhou-Cloud Big Data. This essentially means that a state-owned firm now has access to all the iCloud data China-based users store, such as photos, notes, emails, and text messages.
[1] https://www.reuters.com/article/us-china-apple-icloud-insigh...
[2] https://mashable.com/article/china-government-apple-icloud-d...
The biggest problem with these big faceless companies is the lack of control and that you can't trust them to do what they say they will.
For example the dark patterns that Google puts around location data controls. You turn it off in one place but unfortunately that's the wrong place so they're still collecting that data.
They say that Nest data will never be combined with other Google data ...until they quietly change their terms and do just that.
Privacy law is like tax law to these firms, something to look for loopholes in.
They published a pseudonymous list of (User, movie, rating), triplets and two lists of (User, movie) tuples. The idea was that people could train a model to predict the ratings that correspond to the two lists of (User, movie) tuples.
You could hand in a guess every day, and would get back data a score on the first list of (User, movie) ratings. In the end, whoever got the best score on the second list got $1,000,000.
It was a really interesting challenge, and some good research came of it, but it turned out that it was pretty easy to de-anonymize users by correlating to e.g. IMDB watch lists.