… of knowledge about privacy inference risks in machine learning, going above and beyond
… game-based definitions. Concretely, • We break down the anatomy of game-based privacy …

… Table 2 presents all the results of training privacy-preserving machine learning models using
our min-max game, for all our datasets. It also compares them with the same models when …

… The basic approach in all game based adversarial learning technique is to … The game
involves the following two stages (we provide a generalization of the statistical classification game …

… privacy issues and solutions for machine learning. The survey covers three categories of
interactions between privacy and machine learning: (i) private machine learning… ] set up a game-…

… Let us note, second, that even for a relatively simple game such as chess, machine learning
has to accept an operational approximation of an assumed target function, rather than the …

… private learning. • We systematize desirable properties to improve the security and privacy of
machine learning (… enabled a computer to defeat a human champion at the game of Go [15]. …

… privacy and empirical risk minimization to quantify the utility components due to privacy and
… In Section II we describe the machine learning technique of Empirical Risk Minimization (…

… Our problem is also related to the problem of adversarial machine learning [5, 30] and
the design of security mechanisms, such as intelligent spam detection algorithms [37, 13, 35], …

… of knowledge about privacy inference risks in machine learning, going above and beyond
… game-based definitions. Concretely, • We break down the anatomy of game-based privacy …

… In this paper, we conceptualize the interactions between privacy and … machine learning,
using the frameworks of empirical risk minimization, differential privacy, and Stackelberg games. …