# On the Compositionality of Dynamic Leakage and Its Application to the Quantification Problem

## TL;DR

This paper introduces two efficient, compositional methods for calculating dynamic information leakage in programs, utilizing BDDs and d-DNNFs, with experimental validation demonstrating their effectiveness.

## Contribution

It proposes novel compositional and parallel methods for dynamic leakage computation, extending prior work with exact and approximate calculations using advanced Boolean formula representations.

## Key findings

- The methods are more efficient than existing approaches.
- Experimental results validate the accuracy and scalability of the proposed methods.
- The compositional approach effectively leverages program structure for leakage analysis.

## Abstract

Quantitative information flow (QIF) is traditionally defined as the expected value of information leakage over all feasible program runs and it fails to identify vulnerable programs where only limited number of runs leak large amount of information. As discussed in Bielova (2016), a good notion for dynamic leakage and an efficient way of computing the leakage are needed. To address this problem, the authors have already proposed two notions for dynamic leakage and a method of quantifying dynamic leakage based on model counting. Inspired by the work of Kawamoto et. al. (2017), this paper proposes two efficient methods for computing dynamic leakage, a compositional method along with the sequential structure of a program and a parallel computation based on the value domain decomposition. For the former, we also investigate both exact and approximated calculations. From the perspective of implementation, we utilize binary decision diagrams (BDDs) and deterministic decomposable negation normal forms (d-DNNFs) to represent Boolean formulas in model counting. Finally, we show experimental results on several examples.