TY - GEN

T1 - Evaluating Multiple Guesses by an Adversary via a Tunable Loss Function

AU - Kurri, Gowtham R.

AU - Kosut, Oliver

AU - Sankar, Lalitha

N1 - Funding Information:
The authors are with the School of Electrical, Computer and Energy Engineering at Arizona State University. Email: gkurri@asu.edu, okosut@asu.edu, lsankar@asu.edu This work is supported in part by NSF grants CIF-1901243, CIF-1815361, and CIF-2007688.
Publisher Copyright:
© 2021 IEEE.

PY - 2021/7/12

Y1 - 2021/7/12

N2 - 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 a-loss, a class of tunable loss functions parameterized by a. It has been shown before that this loss function captures well known loss functions including the exponential loss (a = 1/2), the log-loss (a = 1) and the 0-1 loss (a = ∞). We completely characterize the optimal adversarial strategy and the resulting expected α-loss, thereby recovering known results for a = ∞. 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.

AB - 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 a-loss, a class of tunable loss functions parameterized by a. It has been shown before that this loss function captures well known loss functions including the exponential loss (a = 1/2), the log-loss (a = 1) and the 0-1 loss (a = ∞). We completely characterize the optimal adversarial strategy and the resulting expected α-loss, thereby recovering known results for a = ∞. 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.

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U2 - 10.1109/ISIT45174.2021.9517733

DO - 10.1109/ISIT45174.2021.9517733

M3 - Conference contribution

AN - SCOPUS:85115100341

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 2002

EP - 2007

BT - 2021 IEEE International Symposium on Information Theory, ISIT 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Symposium on Information Theory, ISIT 2021

Y2 - 12 July 2021 through 20 July 2021

ER -