Ibrahim Issa

An operational measure of information leakage

Given two discrete random variables X and Y, an operational approach is undertaken to quantify the “leakage” of information from X to Y. The resulting measure ℒ(X→Y ) is called maximal leakage, and is defined as the multiplicative increase, upon observing Y, of the probability of correctly guessing a randomized function of X, maximized over all such randomized functions. It is shown to be equal to the Sibson mutual information of order infinity, giving the latter operational significance. Its resulting properties are consistent with an axiomatic view of a leakage measure; for example, it satisfies the data processing inequality, it is asymmetric, and it is additive over independent pairs of random variables. Moreover, it is shown that the definition is robust in several respects: allowing for several guesses or requiring the guess to be only within a certain distance of the true function value does not change the resulting measure.

Measuring secrecy by the probability of a successful guess

A secrecy system in which both the legitimate receiver and an eavesdropper are allowed some distortion is studied. The eavesdropper is allowed to make one guess, and the considered secrecy metric is the exponent of the probability that the guess is within an acceptable distortion level. A subsequent notion of information leakage is suggested. In the absence of any shared key between the transmitter and the legitimate receiver, a single-letter characterization of the highest achievable exponent is provided. Moreover, asymptotically-optimal universal strategies for both the primary user and the eavesdropper are demonstrated, where universality means independence of the source statistics. When a secret key is shared between the transmitter and the legitimate receiver, and a rate constraint is imposed, upper and lower bounds on the optimal exponent are given. Certain sufficient conditions for the bounds to meet are provided, and examples where they apply are discussed.

Two-Hop Interference Channels: Impact of Linear Schemes

We consider the two-hop interference channel (IC), which consists of two source-destination pairs communicating with each other via two relays. We analyze the degrees of freedom (DoF) of this network when the relays are restricted to perform linear schemes, and the channel gains are constant (i.e., slow fading). We show that, somewhat surprisingly, by using vector-linear strategies at the relays, it is possible to achieve 4/3 sum-DoF when the channel gains are real. The key achievability idea is to alternate relaying coefficients across time, to create different end-to-end interference structures (or topologies) at different times. Although each of these topologies has only 1 sum-DoF, we manage to achieve 4/3 by coding across them. Furthermore, we develop a novel outer bound that matches our achievability, hence characterizing the sum-DoF of two-hop ICs with linear schemes. We also generalize the result to the multi-antenna setting, where each node has M antennas, and the relays are restricted to one-shot linear schemes. We further extend the result to the case of complex channel gains, by intuitively viewing each complex node with M antennas as a real node with 2M antennas, and characterize the sum-DoF to be 2M - 1/3.

Two-hop interference channels: Impact of linear time-varying schemes

We consider the two-hop interference channel (IC) with constant real channel coefficients, which consists of two source-destination pairs, separated by two relays. We analyze the achievable degrees of freedom (DoF) of such network when relays are restricted to perform scalar amplify-forward (AF) operations, with possibly time-varying coefficients. We show that, somewhat surprisingly, by providing the flexibility of choosing time-varying AF coefficients at the relays, it is possible to achieve 4/3 sum-DoF. We also develop a novel outer bound that matches our achievability, hence characterizing the sum-DoF of two-hop interference channels with time-varying AF relaying strategies.

Operational definitions for some common information leakage metrics

Maximal leakage from a random variable X to a random variable Y is defined as the multiplicative increase, upon observing Y, of the probability of correctly guessing a randomized function of X, maximized over all such functions [1]. Herein, this guessing framework is used to give operational definitions to common information leakage metrics, including Shannon capacity, maximal correlation, and local differential privacy. Shannon capacity is shown to capture the multiplicative increase of the probability of correct guessing over the restricted set of functions of X that can be reliably reconstructed from Y, hence underestimating leakage. Maximal correlation is shown to capture the multiplicative change in the variance of functions of X, rather than the guessing probability. Local differential privacy is shown to capture the multiplicative increase of the guessing probability of functions of X, maximized over realizations of Y and over distributions Px. Moreover, maximizing over realizations of Y for a fixed Px is shown to yield a valid leakage measure, which is equal to the maximum information rate.

Measuring Secrecy by the Probability of a Successful Guess

A secrecy system in which both the legitimate receiver and an eavesdropper are allowed some distortion is studied. The eavesdropper is allowed to make one guess, and the considered secrecy metric is the exponent of the probability that the guess is within an acceptable distortion level. A subsequent notion of information leakage is suggested. In the absence of any shared key between the transmitter and the legitimate receiver, a single-letter characterization of the highest achievable exponent is provided. Moreover, asymptotically-optimal universal strategies for both the primary user and the eavesdropper are demonstrated, where universality means independence of the source statistics. When a secret key is shared between the transmitter and the legitimate receiver, and a rate constraint is imposed, upper and lower bounds on the optimal exponent are given. Certain sufficient conditions for the bounds to meet are provided, and examples where they apply are discussed.