  We study the information leakage to a guessing adversary in zero-error source
coding. The source coding problem is defined by a confusion graph capturing the
distinguishability between source symbols. The information leakage is measured
by the ratio of the adversary's successful guessing probability after and
before eavesdropping the codeword, maximized over all possible source
distributions. Such measurement under the basic adversarial model where the
adversary makes a single guess and allows no distortion between its estimator
and the true sequence is known as the maximum min-entropy leakage or the
maximal leakage in the literature. We develop a single-letter characterization
of the optimal normalized leakage under the basic adversarial model, together
with an optimum-achieving scalar stochastic mapping scheme. An interesting
observation is that the optimal normalized leakage is equal to the optimal
compression rate with fixed-length source codes, both of which can be
simultaneously achieved by some deterministic coding schemes. We then extend
the leakage measurement to generalized adversarial models where the adversary
makes multiple guesses and allows certain level of distortion, for which we
derive single-letter lower and upper bounds.
