|| Evaluating Multiple Guesses by an Adversary via a Tunable Loss Function
||Gowtham R. Kurri, Oliver Kosut, Lalitha Sankar, Arizona State University, United States|
||Friday, 16 July, 22:00 - 22:20
||Friday, 16 July, 22:20 - 22:40
We consider a problem of guessing, wherein an adversary is interested in knowing the value of the realization of a discrete random variable $X$ on observing another correlated random variable $Y$. The adversary can make multiple (say, $k$) guesses. The adversary's guessing strategy is assumed to minimize $\alpha$-loss, a class of tunable loss functions parameterized by $\alpha$. It has been shown before that this loss function captures well known loss functions including the exponential loss ($\alpha=1/2$), the log-loss ($\alpha=1$) and the $0$-$1$ loss ($\alpha=\infty$). We completely characterize the optimal adversarial strategy and the resulting expected $\alpha$-loss, thereby recovering known results for $\alpha=\infty$. We define an information leakage measure from the $k$-guesses setup and derive a condition under which the leakage is unchanged from a single guess.