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A variety of biological and social science data come in the form of cross-classified tables of counts, commonly referred to as contingency tables. Until recent years the statistical and computational techniques available for the analysis of cross-classified data were quite limited. This book presents some of the recent work on the statistical analysis of cross-classified data using longlinear models, especially in the multidimensional situation

This issue of the Journal of Privacy and Confidentiality consists four papers ondiverse topics related to privacy and confidentiality.

While social networks are now a part of everyday life for the vast majority of people using computers, smartphones, and tablets, privacy is but an afterthought. Google+ has in excess of 100 million users a month while Facebook has topped 1 billion. Other more specialized networks such as Linked-in add to the fray. But from a privacy perspective the biggest concern for users should be the efforts to integrate the networking apps into all other forms of online activity as well as the constant effort to link additional data to network information, in addition to the network owners' efforts to market that information to third party vendors. Further, Facebook and other networking sites have already begun to build search capabilities and other facilities into their systems that extend the information they collect from users even further. What protections do users really have?

This issue of the Journal of Privacy and Confidentiality focuses on four technical contributions, three of which deal with different aspects of efforts to produce "sanitized" synthetic statistical databases or sanitized responses to queries. The first two of these papers focus on the utility of sanitized databases based on the technique of multiple imputation, the third deals with intruder-styled attacks on output perturbation sanitizers. The fourth addresses a different problem regarding access to, and the use of, web-based data via anonymous authentication.

This issue of the Journal of Privacy and Confidentiality focuses on privacy in com- mercial and public data. Issues regarding the privacy of data collected online and from mobile devices appear in the news almost daily. As Facebook has grown to encompass almost 600 million users, its privacy settings and policies have come under consider- able scrutiny. For an excellent graphical characterization of Facebook privacy we refer our readers to http://www.mattmckeon.com/facebook-privacy/ . Similarly, revelations regarding how Apple iPhones and Google Android phones have kept logs of users' precise location information have raised major concerns about privacy of individual information

In this issue of the Journal of Privacy and Confidentiality , we continue our focus on the technical aspects of research methodologies and activities in the areas of privacy, confidentiality, and disclosure limitation through a trio of articles, but we begin by reproducing related materials from some other sources on broader aspects of the topics.

This article addresses the process through which the federal statistical system may be restored to its appropriate place for providing high-quality data and information, in response to the damage caused by the budgetary woes that agencies have suffered in recent years. The article describes how this work will facilitate moving the country forward, both on its path to economic recovery and to a position of leadership internationally. Four crucial topics are examined: independence, integrity, integration, and innovation. In combination these characteristics will ensure the availability of statistical data of high quality and integrity for informed policy decisions and legislative action.

Sample survey weights represent a standard part of the survey statistician's repertoire, but they remain a mystery for many of those who work with survey data. They also pose a potential challenge for confidentiality protection. Much has been written about how to use weights in statistical analyses since the basic idea of weighted estimates|weighting units inversely proportional to their probability of selection|emerged from the classic paper by Horvitz and Thompson. Very little has been written about the effect of releasing survey weights on the confidentiality of survey data. The two topics are inextricably intertwined. This paper discusses both, largely from the model-based perspective, and explains what is claimed and/or known about the issue of confidentiality protection. The paper also provides a prescription for ways to deal with weights both for analysis and for disclosure limitation.

This article addresses the process through which the federal statistical system may be restored to its appropriate place for providing high-quality data and information, in response to the damage caused by the budgetary woes that agencies have suffered in recent years. The article describes how this work will facilitate moving the country forward, both on its path to economic recovery and to a position of leadership internationally. Four crucial topics are examined: independence, integrity, integration, and innovation. In combination these characteristics will ensure the availability of statistical data of high quality and integrity for informed policy decisions and legislative action. [Reprinted by permission of Sage Publications Inc., copyright The American Academy of Political and Social Science.]

A Glimpse at the Future of Social Science Statistical Data: New Forms of Data Analysis, New Type of Access, and New Issues for Data Providers

AbstractInextricably entwined with research methods are the quantitative skills required to develop, manage, and analyze the complex data sets that almost always characterize evaluation projects. Büt is there any common core of quantitative skills that all evaluation researchers should possess? And should quantitative skills be taught within the context of an evaluation research program?