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My professional relationship with Steve began in the early 1990s, when I came to NISS as Associate Director and he was a member of the Board of Trustees. We sometimes disagreed, or perhaps more accurately, I failed to grasp his wisdom. Something must have worked, though, because Steve also chaired the committee that selected me to be Director of NISS.
Our scientific collaboration arose in late 1990s, when I was PI, and he co-PI, on two grants from NSF's Digital Government initiative. These grants, as did the entire collaboration, stemmed from Steve's fervent belief that deep mathematics can be brought to bear on pressing personal and societal problems. The first had to do with web-based query systems now known as restricted data access systems (RDAS), and specifically with table servers. We were frontiersmen together in formulating and applying risk-utility frontiers, released table frontiers and unreleasable table frontiers.
With his usual prescience, Steve knew before data breaches were daily news that privacy and confidentiality are major concerns. We wrote only a few papers together, but we exchanged sometimes wildly complementary ideas for more than twenty years. I still remember a meeting with a number of federal statistical agencies at which what I proposed as a risk measure was exactly what Steve construed as a utility measure.
From the science grew a multi-year, multi-continent friendship that drew in Joyce and Senora as well. It mattered not whether the last encounter was three weeks or three years ago. Sadly, only one of the four of us now remains, but in keeping with the advice of Dr. Seuss, instead of crying because what ended, I smile because what happened.

Over the past several years, the National Institute of Statistical Sciences (NISS) has developed methodology to perform statistical analyses that, in effect, integrate data in multiple, distributed databases, but without literally bringing the data together in one place. In this paper, we summarize that research, but focus on issues that are not understood. These include inability to perform exploratory analyses and visualizations, protections against dishonest participants, inequities between database owners and lack of measures of risk and utility.

Statistical agencies frequently publish microdata that have been altered to protect condentiality. Such data retain utility for many types of broad analyses but can yield biased or insufficiently precise results in others. Research access to de-identied versions of the restricted-use data with little or no alteration is often possible, albeit costly and time-consuming. We investigate the advantages and disadvantages of public-use and restricted-use data from the American Community Survey (ACS) in constructing a wage index. The public-use data used were Public Use Microdata Samples, while the restricted-use data were accessed via a Federal Statistical Research Data Center. We discuss the advantages and disadvantages of each data source and compare estimated CWIs and standard errors at the state and labor market levels. We find the results from the publicly available data are generally good relative to the restricted-use data, with greater similarity for larger areas and less similarity for smaller areas. Standard errors are higher in the public-used data but may still be underestimated.

Recent technological advances enable the collection of huge amounts

of data. Commonly, these data are generated, stored, and owned by multiple entities

that are unwilling to cede control of their data. This distributed environment

requires statistical tools that can produce correct results while preserving data privacy.

Privacy-preserving protocols have been proposed to solve specific statistical

analysis such as linear regression, clustering, and classification. In this paper, we

present methods and protocols for privacy-preserving maximum likelihood estimation

in general settings. We discuss both horizontally and vertically partitioned

data, and propose procedures that allow participating parties to withdraw from

the joint computation. Logistic regression is used to demonstrate our method.

On May 1–2, 2008, the National Center for Health Statistics/CDC (NCHS) and the National Institute of Statistical Sciences (NISS) co-sponsored a workshop "Data Confi- dentiality: The Next Five Years," which was held at NCHS' headquarters in Hyattsville, MD. The purpose of the workshop was to bring together the academic and federal data confidentiality research communities, including mathematicians, mathematical statisticians, statisticians, computer scientists, cryptographers, and federal agency "owners" of data confidentiality problems in order to:

Identify important unsolved research problems associated with data confidentiality;
Articulate a research agenda that both addresses those problems and responds to current and emerging needs among federal statistical agencies;
Discuss and catalyze the kinds of collaborations among mathematicians, mathematical statisticians, statisticians, computer scientists, cryptographers, domain scientists, and data-owning agencies that are needed to pursue the research agenda.

When releasing microdata to the public, data disseminators typically alter the original data to protect the confidentiality of database subjects' identities and sensitive attributes. However, such alteration negatively impacts the utility (quality) of the released data. In this paper, we present quantitative measures of data utility for masked microdata, with the aim of improving disseminators' evaluations of competing masking strategies. The measures, which are global in that they reflect similarities between the entire distributions of the original and released data, utilize empirical distribution estimation, cluster analysis, and propensity scores. We evaluate the measures using both simulated and genuine data. The results suggest that measures based on propensity score methods are the most promising for general use.