It takes a lot of time and effort to decide how to best explain something. Thank you, this was a wonderful read!
When machine learning tells the wrong story
11–20 of 32 posts
Re: When machine learning tells the wrong story
#12Wonderful article. I never thought I'll be able to understand side-channel attacks so easily.
The article read like a murder mystery where you know who the villain is right in the beginning but you need to find it how they did it!
Marked as favorite.
Re: When machine learning tells the wrong story
#13Earlier quoted context omitted.
My suspicion is that io_uring itself mitigates syscall overhead but doesn't do anything to change interrupts. You could probably do things at the OS level to change interrupt behavior in a way that would mitigate this attack significantly, I'll need to read the paper to see if they discuss this.
Correct. Probably the only way to mitigate interrupt stuff today is what they mentioned - you inject noise into the system intentionally with their example being to make network requests to local addresses. Fundamentally though the challenge is that if you start doing that, you probably start degrading performance fairly quickly for your neighbors. It’s really hard to balance mitigations that retain good performance.…
Let me correct something I stated. To get this correct you still need a timer which you can inject noise into to screw up measurements in theory.
Re: When machine learning tells the wrong story
#14This was interesting, well written and not difficult to understand, and we need more stuff like that. Thank you!
Re: When machine learning tells the wrong story
#15Great piece of technical writing.
I hope when Jack is in Oxford he'll also visit Cambridge to give a guest talk in the late Ross Anderson's former group.
Re: When machine learning tells the wrong story
#16I wonder if adopting io_uring on Linux might allow a browser to preserve the privacy a little, in this specific case. (Though it is very hard to get right, unfortunately.)
My suspicion is that io_uring itself mitigates syscall overhead but doesn't do anything to change interrupts. You could probably do things at the OS level to change interrupt behavior in a way that would mitigate this attack significantly, I'll need to read the paper to see if they discuss this.
I come from embedded audio programming, where e.g. the variable loads of UI code can be problematic (=audible) for audio quality if you don't do things right.
Maybe we need to do things the other way around? So instead of trying to mask everything we are doing, we run browsers/tabs in a processing environment where the noise can't be measured because it does not occur during the same time window. In audio that is done by using a high priority fixed timer that interrupts the rest of the processing.
My OS knowledge is too marginal to know whether that would be truly feasible, but I can't help to think: yeah it is possible to fix that on a more fundamental level.
Re: When machine learning tells the wrong story
#17Very interesting and well-explained. Given that the research has been out for two years, any interested data collectors have considered this! Forget hackers, this an exploit for enterprises and governments! Could websites concerned with privacy deploy a package that triggers interrupts randomly? Could a browser extension do it for every site?
Websites doing this would have to be careful about it: they might become the only website triggering a lot of interrupts randomly, which then makes them easy to identify.
Our countermeasure which triggers interrupts randomly is implemented as a browser extension, the source code for which is available here: https://github.com/jackcook/bigger-fish
I'm not sure I would recommend it for daily use though, I think our tests showed it slowed page load times down by about 10%.
Re: When machine learning tells the wrong story
#18So well written, if you were not a successful researcher, I'd suggest you go into writing. That was a pleasure to read.
Re: When machine learning tells the wrong story
#19Good article, neat research behind it.
I think the paper's contributions really don't have anything to do with ML; it's about the new side channel with interrupts, which is a cool find. ML just gets more people to read it, which I guess is ok. I mean, you could just use "statistics" here in much the same way.
I remember an advisor once telling me: once you figure out what a paper is really about, rewrite it, and remove the stuff you used to think it was about. The title of this paper should be about the new side channel, not about the ML story, imho.
But this is just a nitpick. Great work!
Re: When machine learning tells the wrong story
#20Great read!
Tangent and the smallest of nitpicks about the page: the element's styling with a line of big dots confused me into thinking it was the position indicator of an image carousel!