How much information does a side channel "leak"? Despite decades of work on these channels, including the development of many sophisticated mitigation mechanisms for specific side channels, the fundamental question of how to measure the key quantity of interest---leakage---has received surprisingly little attention. Many metrics have been used in the literature, but these metrics either lack a cogent operational justification or mislabel systems that are obviously insecure as secure. Mutual information, in particular, while often used as a leakage measure in side channels, does not have a clear operational interpretation in this context.
Focusing on the problem of protecting sensitive information such as passwords and keys, I propose a new metric called "maximal leakage," provide an operational justification for it, show how it can be computed in practice, and discuss how it relates to existing metrics, including mutual information, differential privacy, and a certain under-appreciated metric in the computer science literature. I also present a solution to Shannon's cipher system under this metric, which can be applied to design optimal side channel mitigation strategies. Among other findings, I show that mutual information underestimates leakage in side channels while local differential privacy overestimates it.
This is joint work with Ibrahim Issa and Sudeep Kamath.
Aaron Wagner is a professor in the School of Electrical and Computer Engineering at Cornell University. He received the B.S. degree from the University of Michigan, Ann Arbor, and the M.S. and Ph.D. degrees from the University of California, Berkeley. During the 2005-2006 academic year, he was a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign and a Visiting Assistant Professor in the School of Electrical and Computer Engineering at Cornell. He has received the NSF CAREER award, the David J. Sakrison Memorial Prize from the U.C. Berkeley EECS Dept., the Bernard Friedman Memorial Prize in Applied Mathematics from the U.C. Berkeley Dept. of Mathematics, the James L. Massey Research and Teaching Award for Young Scholars from the IEEE Information Theory Society and teaching awards at the Department, College, and University level at Cornell.