Re:Intel Performance Hit 5x Harder Than AMD After Spectre, M...
by alkonaut on Tuesday May 21, 2019 @08:46AM
(#19969601)
Yes it would be extremely difficult to make it so slow that it's not a viable attack vector. So long as it has a known distribution it's not random enough because the mean is the unknown value. I remember too little statistics to understand whether it's possible to add randomness such that the measurements are not distributed in a sense that makes the underlying value not be the mean. It does sound impossible at least.
Re:Intel Performance Hit 5x Harder Than AMD After Spectre, M...
by celrod on Tuesday May 21, 2019 @10:42AM
(#19970566)
Cauchy distribution.
https://en.m.wikipedia.org/wiki/Cauchy_distribution
The mean of a sample of cauchy(m,s) variables has that same cauchy(m,s) distribution, regardless of sample size.
Meaning to matter how many samples you take, the mean (of the samples) is just as variable as an individual sample.
The mean and variance of the distribution (equivalently, of infinite samples) are undefined. The Cauchy is equivalent to a t distribution with 1 degree of freedom.
Infinite variability will be undesirable for lots of reasons though.
For a simple example, imagine you want to distinguish a 1ms difference in the execution time of some operation. Without noise, you just have to time it; now let's randomly add either nothing or 1ms to the operation time, so the "fast" operation will take either +0ms or +1ms, and the "slow" operation will take either +1ms or +2ms. But if you repeat the same operation several times and average the execution times, the "fast" operation will take an average of +0.5ms, and the "slow" operation will take an average of +1.5ms. As you can see, in this simple example the random noise averaged itself to a normal distribution, and the original signal is still visible on top of it.