This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients’ pri...

Integrating the outputs of multiple classiiers via combiners or meta-learners has led to substantial improvements in several diicult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classiiers. Based on a mathematical modeling of how the...

In this paper, a voice activity detector (VAD) for variable rate speech coding is decomposed into two parts, a decision rule and a background noise statistic estimator, which are analysed separately by applying a statistical model. A robust decision rule is derived from the generalized likelihood ratio test by assuming that the noise statistics are known a priori. To estimate the time-varying n...

We consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based on the minimum density power divergence estimators instead of the maximum likelihood estimators. An extensive study of their robustness properties is given though the influence functions as well as the chi-square infla...

Relations between anisotropic diiusion and robust statistics are described in this paper. We show that anisotropic diiusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The \edge-stopping" function in the anisotropic diiusion equation is closely related to the error norm and innuence function in the robust estimation framework. T...

This is the second paper of a two part tutorial on the fundamentals of univariate time series ltering using order statistics where both temporal and rank orderings are considered jointly This second paper focuses on order statistic selection lters where the lter output is restricted to be one of the input samples In particular we treat class of Weighted Order Statistic WOS lters and the more ge...

Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the data, occasional large-scale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techniques, some borrowed from the statistics literature and others described in the computer vision literature, ...