33,890 research outputs found
Privacy Games: Optimal User-Centric Data Obfuscation
In this paper, we design user-centric obfuscation mechanisms that impose the
minimum utility loss for guaranteeing user's privacy. We optimize utility
subject to a joint guarantee of differential privacy (indistinguishability) and
distortion privacy (inference error). This double shield of protection limits
the information leakage through obfuscation mechanism as well as the posterior
inference. We show that the privacy achieved through joint
differential-distortion mechanisms against optimal attacks is as large as the
maximum privacy that can be achieved by either of these mechanisms separately.
Their utility cost is also not larger than what either of the differential or
distortion mechanisms imposes. We model the optimization problem as a
leader-follower game between the designer of obfuscation mechanism and the
potential adversary, and design adaptive mechanisms that anticipate and protect
against optimal inference algorithms. Thus, the obfuscation mechanism is
optimal against any inference algorithm
Heterogeneity in the Effect of Common Shocks on Healthcare Expenditure Growth
Health care expenditure growth is affected by important unobserved common shocks such as technological innovation, changes in sociological factors, shifts in preferences and the epidemiology of diseases. While common factors impact in principle all countries, their effect is likely to differ across countries. To allow for unobserved heterogeneity in the effects of common shocks, we estimate a panel data model of health care expenditure growth in 34 OECD countries over the years 1980 to 2012 where the usual fixed or random effects are replaced by a multifactor error structure. We address model uncertainty with Bayesian Model Averaging, to identify a small set of important expenditure drivers from 43 potential candidates. We establish 16 significant drivers of healthcare expenditure growth, including growth in GDP per capita and in insurance premiums, changes in financing arrangements and some institutional characteristics, expenditures on pharmaceuticals, population aging, costs of health administration, and inpatient care. Our approach allows us to derive estimates that are less subject to bias than in previous analyses, and provide robust evidence to policy makers on the drivers that were most strongly associated with the growth in health care expenditures over the past 32 years
Optimal pricing using online auction experiments: A P\'olya tree approach
We show how a retailer can estimate the optimal price of a new product using
observed transaction prices from online second-price auction experiments. For
this purpose we propose a Bayesian P\'olya tree approach which, given the
limited nature of the data, requires a specially tailored implementation.
Avoiding the need for a priori parametric assumptions, the P\'olya tree
approach allows for flexible inference of the valuation distribution, leading
to more robust estimation of optimal price than competing parametric
approaches. In collaboration with an online jewelry retailer, we illustrate how
our methodology can be combined with managerial prior knowledge to estimate the
profit maximizing price of a new jewelry product.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS503 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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