Average Distortion Constraint Research Articles

Privacy Against a Hypothesis Testing Adversary

Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distribution is studied. The original data sequence is assumed to come from one of the two known distributions, and the privacy leakage is measured by the probability of error of the binary hypothesis test carried out by the AD. A management unit (MU) is allowed to manipulate the original data sequence in an online fashion while satisfying an average distortion constraint. The goal of the MU is to maximize the minimal type II probability of error subject to a constraint on the type I probability of error assuming an adversarial Neyman–Pearson test, or to maximize the minimal error probability assuming an adversarial Bayesian test. The asymptotic exponents of the maximum minimal type II probability of error and the maximum minimal error probability are shown to be characterized by a Kullback–Leibler divergence rate and a Chernoff information rate, respectively. Privacy performances of particular management policies, the memoryless hypothesis-aware policy and the hypothesis-unaware policy with memory, are compared. The proposed formulation can also model adversarial example generation with minimal data manipulation to fool classifiers. At last, the results are applied to a smart meter privacy problem, where the user’s energy consumption is manipulated by adaptively using a renewable energy source in order to hide user’s activity from the energy provider.

Zero-Delay and Causal Single-User and Multi-User Lossy Source Coding with Decoder Side Information

We consider zero-delay, single-user, and multi-user source coding with an average distortion constraint and decoder side information. The zero-delay constraint translates into causal (sequential) encoder and decoder pairs as well as the use of instantaneous codes. For the single-user setting, we show that optimal performance is attained by time sharing at most two scalar encoder-decoder pairs, that use zero-error side information codes. Side information look-ahead is shown to be useless in this setting. Furthermore, we show that even without delay constraints, if either the encoder or decoder are restricted a priori to be scalar, the performance loss cannot be compensated by the other component, which can be scalar as well without further loss. Finally, we show that the multi-terminal source coding problem can be solved in the zero-delay regime and the rate-distortion region is provided.

Energy Management Policies for Energy-Neutral Source-Channel Coding

In cyber-physical systems where sensors measure the temporal evolution of a given phenomenon of interest and radio communication takes place over short distances, the energy spent for source acquisition and compression may be comparable with that used for transmission. Additionally, in order to avoid limited lifetime issues, sensors may be powered via energy harvesting and thus collect all the energy they need from the environment. This work addresses the problem of energy allocation over source acquisition/compression and transmission for energy-harvesting sensors. At first, focusing on a single-sensor, energy management policies are identified that guarantee a maximal average distortion while at the same time ensuring the stability of the queue connecting source and channel encoders. It is shown that the identified class of policies is optimal in the sense that it stabilizes the queue whenever this is feasible by any other technique that satisfies the same average distortion constraint. Moreover, this class of policies performs an independent resource optimization for the source and channel encoders. Analog transmission techniques as well as suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied for performance comparison. The problem of optimizing the desired trade-off between average distortion and delay is then formulated and solved via dynamic programming tools. Finally, a system with multiple sensors is considered and time-division scheduling strategies are derived that are able to maintain the stability of all data queues and to meet the average distortion constraints at all sensors whenever it is feasible.

Variable-rate universal block source coding subject to a fidelity constraint

Variable-rate universal source codes are data compression schemes that are optimum for the coding of a collection of sources (e.g., a source with unknown parameters) subject to a fixed average distortion constraint. Existence of variable-rate universal source codes is demonstrated for very general classes of sources and distortion measures.

Variable rate coding for nonergodic sources and classes of ergodic sources subject to a fidelity constraint

This paper is concerned with two problems in the theory of source coding subject to a maximum average distortion constraint. The first problem involves the coding of a nonergodic discrete time source and the second involves coding for a class of ergodic discrete time sources. Coding theorems are given for both of these situations for very general source alphabets. The codes that are obtained here will, in general, be variable length codes so that average code rate and average distortion are the measures of performance.