From reading this paper, this appears to allow you to guess what a computer is doing by determining which memory regions are actively in use by making use of the high precision timer.
By training it on something like network or mouse pointer data, you can determine whether a user or network is active based on cache activity patterns. You can't resolve much else other than this high-level view of which segments are in use, but they've been pretty creative figuring out what this can tell you.
It's possible that this could be used as another method to fingerprint Tor users, or confirm that a given Tor user corresponds to a given insecure user.
This paper is really more about the fact that basically everyone is automatically vulnerable to this attack. It doesn't really address the applications, but:
> [..] the attacker must now correlate the cache sets he has profiled to data or code locations belonging to the victim. This learning/classification problem was addressed earlier by Zhang et al. in [25] and by Yarom et al. in [23], where various machine learning methods such as SVM were used to derive meaning from the output of cache latency measurements.
It can probably also be put to less nefarious uses. Here's one that comes to mind for a web based chat client: You can use it to detect if a user is currently at his/her desktop or away. Unless the chat window is the current active window, I think this would be hard/impossible to do otherwise.
By training it on something like network or mouse pointer data, you can determine whether a user or network is active based on cache activity patterns. You can't resolve much else other than this high-level view of which segments are in use, but they've been pretty creative figuring out what this can tell you.
It's possible that this could be used as another method to fingerprint Tor users, or confirm that a given Tor user corresponds to a given insecure user.