You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Hypothesis testing is a statistical inference framework for determining the true distribution among a set of possible distributions for a given dataset. Privacy restrictions may require the curator of the data or the respondents themselves to share data with the test only after applying a randomizing privacy mechanism. This work considers mutual information (MI) as the privacy metric for measuring leakage. In addition, motivated by the Chernoff-Stein lemma, the relative entropy between pairs of distributions of the output (generated by the privacy mechanism) is chosen as the utility metric. For these metrics, the goal is to find the optimal privacy-utility trade-off (PUT) and the corresponding optimal privacy mechanism for both binary and m-ary hypothesis testing. Focusing on the high privacy regime, Euclidean information-theoretic approximations of the binary and m-ary PUT problems are developed. The solutions for the approximation problems clarify that an MI-based privacy metric preserves the privacy of the source symbols in inverse proportion to their likelihoods.

Cited Abstract(s):
[1]: Ensuring the usefulness of electronic data sources while providing necessary privacy guarantees is an important unsolved problem. This problem drives the need for an analytical framework that can quantify the privacy of personally identifiable information while still providing a quantifiable benefit (utility) to multiple legitimate information consumers. This paper presents an information-theoretic framework that promises an analytical model guaranteeing tight bounds of how much utility is possible for a given level of privacy and vice-versa. Specific contributions include: 1) stochastic data models for both categorical and numerical data; 2) utility-privacy tradeoff regions and the encoding (sanization) schemes achieving them for both classes and their practical relevance; and 3) modeling of prior knowledge at the user and or data source and optimal encoding schemes for both cases.

[2]: The solutions offered to-date for end-user privacy in smart meter measurements, a well-known challenge in the smart grid, have been tied to specific technologies such as batteries or assumptions on data usage without quantifying the loss of benefit (utility) that results from any such approach. Using tools from information theory and a hidden Markov model for the measurements, a new framework is presented that abstracts both the privacy and the utility requirements of smart meter data. This leads to a novel privacy-utility tradeoff problem with minimal assumptions that is tractable. For a stationary Gaussian model of the electricity load, it is shown that for a desired mean-square distortion (utility) measure between the measured and revealed data, the optimal privacy-preserving solution: i) exploits the presence of high-power but less private appliance spectra as implicit distortion noise, and ii) filters out frequency components with lower power relative to a distortion threshold; this approach encompasses many previously proposed approaches to smart meter privacy.

[3]: 

[4]: We propose a practical methodology to protect a user's private data, when he wishes to publicly release data that is correlated with his private data, in the hope of getting some utility. Our approach relies on a general statistical inference framework that captures the privacy threat under inference attacks, given utility constraints. Under this framework, data is distorted before it is released, according to a privacy-preserving probabilistic mapping. This mapping is obtained by solving a convex optimization problem, which minimizes information leakage under a distortion constraint. We address a practical challenge encountered when applying this theoretical framework to real world data: the optimization may become untractable and face scalability issues when data assumes values in large size alphabets, or is high dimensional. Our work makes two major contributions. We first reduce the optimization size by introducing a quantization step, and show how to generate privacy mappings under quantization. Second, we evaluate our method on a dataset showing correlations between political views and TV viewing habits, and demonstrate that good privacy properties can be achieved with limited distortion so as not to undermine the original purpose of the publicly released data, e.g. recommendations.

[5]: 

[6]: We propose a general statistical inference framework to capture the privacy threat incurred by a user that releases data to a passive but curious adversary, given utility constraints. We show that applying this general framework to the setting where the adversary uses the self-information cost function naturally leads to a non-asymptotic information-theoretic approach for characterizing the best achievable privacy subject to utility constraints. Based on these results we introduce two privacy metrics, namely average information leakage and maximum information leakage. We prove that under both metrics the resulting design problem of finding the optimal mapping from the user's data to a privacy-preserving output can be cast as a modified rate-distortion problem which, in turn, can be formulated as a convex program. Finally, we compare our framework with differential privacy.

[7]: t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.

[8]: We focus on the privacy-accuracy tradeoff encountered by a user who wishes to release some data to an analyst, that is correlated with his private data, in the hope of receiving some utility. We rely on a general statistical inference framework, under which data is distorted before its release, according to a probabilistic privacy mechanism designed under utility constraints. Using recent results on maximal correlation and hyper-contractivity of Markov processes, we first propose novel techniques to design utility-aware privacy mechanisms against inference attacks, when only partial statistical knowledge of the prior distribution linking private data and data to be released is available. We then propose optimal privacy mechanisms in the class of additive noise mechanisms, for both continuous and discrete released data, whose design requires only knowledge of second-order moments of the data to be released. We then turn our attention to multi-agent systems, where multiple data releases occur, and use tensorization results of maximal correlation to analyze how privacy guarantees compose after collusion or composition. Finally, we show the relationship between different existing privacy metrics, in particular divergence privacy, and differential privacy.

[9]: The rate-privacy function is defined in Asoodeh as a tradeoff between privacy and utility in a distributed private data system in which both privacy and utility are measured using mutual information. Here, we use maximal correlation in lieu of mutual information in the privacy constraint. We first obtain some general properties and bounds for maximal correlation and then modify the rate-privacy function to account for the privacy-constrained estimation problem. We find a bound for the utility in this problem when the maximal correlation privacy is set to some threshold @math and construct an explicit privacy scheme which achieves this bound.

[10]: Lower bounds for the average probability of error of estimating a hidden variable X given an observation of a correlated random variable Y, and Fano's inequality in particular, play a central role in information theory. In this paper, we present a lower bound for the average estimation error based on the marginal distribution of X and the principal inertias of the joint distribution matrix of X and Y. Furthermore, we discuss an information measure based on the sum of the largest principal inertias, called k-correlation, which generalizes maximal correlation. We show that k-correlation satisfies the Data Processing Inequality and is convex in the conditional distribution of Y given X. Finally, we investigate how to answer a fundamental question in inference and privacy: given an observation Y, can we estimate a function f(X) of the hidden random variable X with an average error below a certain threshold? We provide a general method for answering this question using an approach based on rate-distortion theory.

[11]: We investigate the problem of intentionally disclosing information about a set of measurement points X (useful information), while guaranteeing that little or no information is revealed about a private variable S (private information). Given that S and X are drawn from a finite set with joint distribution pS,X, we prove that a non-trivial amount of useful information can be disclosed while not disclosing any private information if and only if the smallest principal inertia component of the joint distribution of S and X is 0. This fundamental result characterizes when useful information can be privately disclosed for any privacy metric based on statistical dependence. We derive sharp bounds for the tradeoff between disclosure of useful and private information, and provide explicit constructions of privacy-assuring mappings that achieve these bounds.
MI has been amply used as a measure for quantifying information leakage within the information-theoretic privacy literature (cf. [1] [2] [6] [4] [11] [7] [3] and the references therein). The connection between MI-based metrics and other privacy metrics has been studied, for example, by Makhdoumi and Fawaz [8] . In the present paper, we approximate MI by the chi-squared divergence which, in turn, also posses interesting estimation-theoretic properties [10] . An exploration of the role of chi-squared related metrics in privacy has appeared in the work of [9] [5] .