On quantitative dynamic data flow tracking
Enrico Lovat, Johan Oudinet, Alexander Pretschner · 2014
We present a non-probabilistic model for dynamic quantitative data flow tracking. Estimations of the amount of data stored in a particular representation at runtime - a file, a window, a network packet - enable the adoption of fine-grained policies which authorize or prohibit partial leaks of data. We prove the correctness of the estimations, provide an implementation that we evaluate w.r.t. precision and performance, and analyze one instantiation at the OS level.