On the Measurement of Privacy as an Attacker's Estimation Error

A wide variety of privacy metrics have been proposed in the literature to evaluate the level of protection offered by privacy enhancing-technologies. Most of these metrics are specific to concrete systems and adversarial models, and are difficult to generalize or translate to other contexts. Furthermore, a better understanding of the relationships between the different privacy metrics is needed to enable more grounded and systematic approach to measuring privacy, as well as to assist systems designers in selecting the most appropriate metric for a given application. In this work we propose a theoretical framework for privacy-preserving systems, endowed with a general definition of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. We show that our framework permits interpreting and comparing a number of well-known metrics under a common perspective. The arguments behind these interpretations are based on fundamental results related to the theories of information, probability and Bayes decision.Comment: This paper has 18 pages and 17 figure

A Method for Individual Source Brightness Estimation in Single- and Multi-band Data

We present a method of reliably extracting the flux of individual sources from sky maps in the presence of noise and a source population in which number counts are a steeply falling function of flux. The method is an extension of a standard Bayesian procedure in the millimeter/submillimeter literature. As in the standard method, the prior applied to source flux measurements is derived from an estimate of the source counts as a function of flux, dN/dS. The key feature of the new method is that it enables reliable extraction of properties of individual sources, which previous methods in the literature do not. We first present the method for extracting individual source fluxes from data in a single observing band, then we extend the method to multiple bands, including prior information about the spectral behavior of the source population(s). The multi-band estimation technique is particularly relevant for classifying individual sources into populations according to their spectral behavior. We find that proper treatment of the correlated prior information between observing bands is key to avoiding significant biases in estimations of multi-band fluxes and spectral behavior, biases which lead to significant numbers of misclassified sources. We test the single- and multi-band versions of the method using simulated observations with observing parameters similar to that of the South Pole Telescope data used in Vieira, et al. (2010).Comment: 11 emulateapj pages, 3 figures, revised to match published versio

Cosmological parameter inference with Bayesian statistics

Bayesian statistics and Markov Chain Monte Carlo (MCMC) algorithms have found their place in the field of Cosmology. They have become important mathematical and numerical tools, especially in parameter estimation and model comparison. In this paper, we review some fundamental concepts to understand Bayesian statistics and then introduce MCMC algorithms and samplers that allow us to perform the parameter inference procedure. We also introduce a general description of the standard cosmological model, known as the $\Lambda$CDM model, along with several alternatives, and current datasets coming from astrophysical and cosmological observations. Finally, with the tools acquired, we use an MCMC algorithm implemented in python to test several cosmological models and find out the combination of parameters that best describes the Universe.Comment: 30 pages, 17 figures, 5 tables; accepted for publication in Universe; references adde

β\beta-Decay Spectrum, Response Function and Statistical Model for Neutrino Mass Measurements with the KATRIN Experiment

The objective of the Karlsruhe Tritium Neutrino (KATRIN) experiment is to determine the effective electron neutrino mass $m(\nu_\text{e})$ with an unprecedented sensitivity of $0.2\,\text{eV}$ (90\% C.L.) by precision electron spectroscopy close to the endpoint of the $\beta$ decay of tritium. We present a consistent theoretical description of the $\beta$ electron energy spectrum in the endpoint region, an accurate model of the apparatus response function, and the statistical approaches suited to interpret and analyze tritium $\beta$ decay data observed with KATRIN with the envisaged precision. In addition to providing detailed analytical expressions for all formulae used in the presented model framework with the necessary detail of derivation, we discuss and quantify the impact of theoretical and experimental corrections on the measured $m(\nu_\text{e})$. Finally, we outline the statistical methods for parameter inference and the construction of confidence intervals that are appropriate for a neutrino mass measurement with KATRIN. In this context, we briefly discuss the choice of the $\beta$ energy analysis interval and the distribution of measuring time within that range.Comment: 27 pages, 22 figures, 2 table

Median Statistics, H_0, and the Accelerating Universe

(Abridged) We develop median statistics that provide powerful alternatives to chi-squared likelihood methods and require fewer assumptions about the data. Applying median statistics to Huchra's compilation of nearly all estimates of the Hubble constant, we find a median value H_0=67 km/s/Mpc. Median statistics assume only that the measurements are independent and free of systematic errors. This estimate is arguably the best summary of current knowledge because it uses all available data and, unlike other estimates, makes no assumption about the distribution of measurement errors. The 95% range of purely statistical errors is +/- 2 km/s/Mpc. The statistical precision of this result leads us to analyze the range of possible systematic errors in the median, which we estimate to be roughly +/- 5 km/s/Mpc (95% limits), dominating over the statistical errors. A Bayesian median statistics treatment of high-redshift Type Ia supernovae (SNe Ia) apparent magnitude versus redshift data from Riess et al. yields a posterior probability that the cosmological constant Lambda > 0 of 70 or 89%, depending on the prior information used. The posterior probability of an open universe is about 47%. Analysis of the Perlmutter et al. high-redshift SNe Ia data show the best-fit flat-Lambda model favored over the best-fit Lambda = 0 open model by odds of 366:1; corresponding Riess et al. odds are 3:1 (assuming prior odds of 1:1).Median statistics analyses of the SNe Ia data do not rule out a time-variable Lambda model, and may even favor it over a time-independent Lambda and a Lambda = 0 open model.Comment: Significant revisions include discussion of systematic errors in the median of H_0. Accepted for publication in The Astrophysical Journal, v548, February 20, 2001 issue. 47 pages incl. figures and table

Estimation of Upper Limits Using a Poisson Statistic

Bayesian, classical, and extended maximum likelihood approaches to estimation of upper limits in experiments with small numbers of signal events are surveyed. The discussion covers only experiments whose outcomes are well described by a Poisson statistic. A new approach, based on the statistical significance of a signal rather than on the number of events in the signal region, is proposed. A toy model and an example of a recent search for the lepton number violating decay $\tau\to\mu\gamma$ are used to illustrate application of the discussed techniques.Comment: 24 pages, 7 figure