Reliability

This special volume of Statistical Sciences presents some innovative, if not provocative, ideas in the area of reliability, or perhaps more appropriately named, integrated system assessment. In this age of exponential growth in science, engineering and technology, the capability to evaluate the performance, reliability and safety of complex systems presents new challenges. Today's methodology must respond to the ever-increasing demands for such evaluations to provide key information for decision and policy makers at all levels of government and industry--problems ranging from international security to space exploration. We, the co-editors of this volume and the authors, believe that scientific progress in reliability assessment requires the development of processes, methods and tools that combine diverse information types (e.g., experiments, computer simulations, expert knowledge) from diverse sources (e.g., scientists, engineers, business developers, technology integrators, decision makers) to assess quantitative performance metrics that can aid decision making under uncertainty. These are highly interdisciplinary problems. The principal role of statistical sciences is to bring statistical rigor, thinking and methodology to these problems.Comment: Published at http://dx.doi.org/10.1214/088342306000000664 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Database security and confidentiality: Examining disclosure risk vs. data utility through the R-U confidentiality map

Managers of database security must ensure that data access does not compromise the confidentiality afforded data providers, whether individuals or establishments. Recognizing that deidentification of data is generally inadequate to protect confidentiality against attack by a data snooper, managers of information organizations (IOs)—such as statistical agencies, data archives, and trade associations—can implement a variety of disclosure limitation (DL) techniques—such as topcoding, noise addition and data swapping—in developing data products. Desirably, the resulting restricted data have both high data utility U to data users and low disclosure risk R from data snoopers. IOs lack a framework for examining tradeoffs between R and U under a specific DL procedure. They also lack systematic ways of comparing the performance of distinct DL procedures. To provide this framework and facilitate comparisons, the R-U confidentiality map is introduced to trace the joint impact on R and U to changes in the parameters of a DL procedure. Implementation of an R-U confidentiality map is illustrated in the case of multivariate noise addition. Analysis is provided for two important multivariate estimation problems: a data user seeks to estimate linear combinations of means and to estimate regression coefficients. Implications for managers are explored