ttt111222333
Joined 10 years ago
Joined 10 years ago
What I'd like to see, is every device run a small process which can read the user's data. Html then has a syntax that can be interpreted like
{{ solid://mydata/name }}
{{ solid://mydata/profile.jpg }}
{{ solid://mydata/age }}
etc.
That data is on the user's device encrypted. Apps can never read your data, they can only tell your device to display that data.
That way developers cannot read your data, store them in their own databases, and then accidentally get their own database hacked and we are back to square one.
I'd like the data on your device to be encrypted and have some type of homomorphic encryption such that if an app were to show average age of users, then an app would be able to run some sort of `select average(age) from users` but since the encryption is homomorphic, the app never learns information about any individual user. This would apply for machine learning operations too, so that we could get netflix style recommendations of movies, without a company ever learning what movies we liked, our age, etc.
However, I don't know the first thing about homomorphic encryption so I guess I just have to wait until some great soul builds something like this for us.
Did you receive a response? And if so, how long did you wait after taking the test?
If any toronto natives know of some big companies in Toronto with tech jobs please prove me wrong.
That's the spiel at least...
Is it me or is this wording terrible?
Trudeau is not a Conservative. It makes it sound like it is Trudeau's party that caused the problem.
Apparently ternary makes math easier in terms of signs etc. as well. The below article explains it better.
https://dev.to/buntine/the-balanced-ternary-machines-of-sovi...
Given the wealth of experience with binary though I don't think its prudent to jump to ternary...
How is it a benefit that you are no longer able to join data in your separate tables? Is this sentence a mistake?
If you are okay with false positives, you can use a bloom filter and tune the number of false positives you want. I chose a false positive rate of 1 in a million so my data structure was still very accurate in determining whether a password was already hacked.
It only took 30 microseconds to determine if a password was in the list and for size, was at the theoretical limit of 22 bits per element or ~1.5gb.
I originally used a bloom filter which made it 2gb but given a bloom filter was just a sequence of 0s and 1s, I was able to use a golomb coding to shrink it down to 1.5gb.
The time to process the original 24gb however, is something that I could have improved, but I kinda lost interest once I already had something that was at the theoretical minimum size, as well as able to determine a password exists within 30 microseconds.
Anyways take a look if you're interested in trying a different approach: https://github.com/terencechow/PwnedPasswords