Foundations of Quantitative Information Flow: Channels, Cascades, and the Information Order

Authors

Barbara Espinoza Becerra, Florida International UniversityFollow

Document Type

Thesis

Degree

Doctor of Philosophy (PhD)

Major/Program

Computer Science

First Advisor's Name

Geoffrey Smith

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Bogdan Carbunar

Third Advisor's Name

Peter J. Clarke

Fourth Advisor's Name

Dev K. Roy

Fifth Advisor's Name

Jinpeng Wei

Keywords

Secrecy, Quantitative Information Flow, Information Theory

Date of Defense

3-25-2014

Abstract

Secrecy is fundamental to computer security, but real systems often cannot avoid leaking some secret information. For this reason, the past decade has seen growing interest in quantitative theories of information flow that allow us to quantify the information being leaked. Within these theories, the system is modeled as an information-theoretic channel that specifies the probability of each output, given each input. Given a prior distribution on those inputs, entropy-like measures quantify the amount of information leakage caused by the channel.

This thesis presents new results in the theory of min-entropy leakage. First, we study the perspective of secrecy as a resource that is gradually consumed by a system. We explore this intuition through various models of min-entropy consumption. Next, we consider several composition operators that allow smaller systems to be combined into larger systems, and explore the extent to which the leakage of a combined system is constrained by the leakage of its constituents. Most significantly, we prove upper bounds on the leakage of a cascade of two channels, where the output of the first channel is used as input to the second. In addition, we show how to decompose a channel into a cascade of channels.

We also establish fundamental new results about the recently-proposed g-leakage family of measures. These results further highlight the significance of channel cascading. We prove that whenever channel A is composition refined by channel B, that is, whenever A is the cascade of B and R for some channel R, the leakage of A never exceeds that of B, regardless of the prior distribution or leakage measure (Shannon leakage, guessing entropy leakage, min-entropy leakage, or g-leakage). Moreover, we show that composition refinement is a partial order if we quotient away channel structure that is redundant with respect to leakage alone. These results are strengthened by the proof that composition refinement is the only way for one channel to never leak more than another with respect to g-leakage. Therefore, composition refinement robustly answers the question of when a channel is always at least as secure as another from a leakage point of view.

Identifier

FI14040824

Recommended Citation

Espinoza Becerra, Barbara, "Foundations of Quantitative Information Flow: Channels, Cascades, and the Information Order" (2014). FIU Electronic Theses and Dissertations. 1156.
https://digitalcommons.fiu.edu/etd/1156