BALANCING DATA CONFIDENTIALITY AND DATA QUALITY      A two-day short course sponsored by the Joint Program in Survey Methodology

FEBRUARY 23 - 24, 2010
Presented at the Metro Center Marriott, Washington DC

LAWRENCE H. COX

COURSE OBJECTIVES
Ethical survey practice demands that confidential data pertaining to individual persons or entities not be revealed through released data products. Ethical concerns are often reinforced by legislation or regulation, such as the Confidential Information Protection and Statistical Efficiency Act of 2002 (CIPSEA) and the Health Insurance Portability and Accountability Act of 1996 (HIPAA). Confidentiality concerns have been addressed by researchers and government statisticians over several decades, resulting in a suite of increasingly sophisticated and effective methods for statistical disclosure limitation (SDL), several of which have been implemented in software and incorporated in the survey practices of government statistical agencies in the U.S. and abroad.  Until very recently, however, the effects of disclosure limitation methods on data quality, completeness and usability have been largely ignored. The interplay between data confidentiality methods and data quality and usability is the unifying subject of this course.

This course has three objectives: (1) to familiarize the student with statistical disclosure limitation and SDL methods; (2) to examine potential effects of SDL methods on data completeness, quality and usability; and, (3) to present SDL methods that, in addition to protecting confidentiality effectively, limit abbreviation or deterioration in the usability, quality and completeness of the released data product. Practical data quality questions include: What effect does the SDL method have on key statistics? What effect does the SDL method have on the distribution of the original data? How easy are disclosure-limited data to analyze compared to original data? Is analysis based on disclosure-limited data an acceptable substitute for analysis based on original data?

This course will cover the following topics: reasons for confidentiality protection; legal and regulatory requirements, including CIPSEA and HIPAA; legal and administrative solutions for restricting unauthorized access to confidential data; survey methods for restricting released data and for quantifying and limiting disclosure in tabulations, microdata and public use statistical data base query systems; using research data centers and controlled remote access to increase authorized access to confidential data; and, balancing the confidentiality protection provided by SDL methods with their effects on the usability, quality and completeness of released data products. Emphasis will be placed on recognizing disclosure and evaluating the effectiveness of disclosure limitation strategies and their effects on data quality by means of lecture, discussion and simple numeric examples. Classroom notes, mathematical preliminaries, URLs, and references on disclosure limitation will be provided. The course is organized around types of data release—tabulations, microdata, data base query systems—but much of the material, particularly from the first day, is of general relevance. 

WHO SHOULD ATTEND
Most survey data are collected under some assurances of confidentiality protection, yet few survey practitioners, managers, and data users are familiar with disclosure limitation methods and their effects on the quality and ease of use of disclosure-limited data products. Individuals in Federal and State governments, business, universities, and non-profit organizations involved with survey methodology or the preparation or use of survey data will benefit from this course. Statisticians concerned with assessing and preserving data quality will find the course especially useful. Those involved with the introduction of new or redesigned surveys and with CIPSEA or HIPAA will find the material important and timely. The course will address methodological, practical and policy aspects of the problem. Background in elementary statistics and linear algebra is desirable. Key points will be introduced and reinforced by means of simple numeric examples. In-class discussion of the literature will prepare interested students for further study and application of SDL methods.  Rapidly developing areas such as statistical data base query systems, quality-preserving SDL, and secure distributed statistical analysis will be highlighted.

THE INSTRUCTOR
Larry Cox has extensive experience in statistical disclosure and the development and implementation of disclosure limitation methods. He has published numerous papers, delivered many lectures, organized conferences and meetings, and taught several courses in the U.S. and abroad on privacy, confidentiality and statistical disclosure limitation. His research on SDL methods has led to adoption and automation of several of these methods by international statistical organizations for large-scale use. His recent research on quality-preserving SDL methods is at the forefront of this topic. Dr. Cox's professional experience includes consulting, teaching, and research. He has served as senior research statistician for three government agencies and as Director of the Board on Mathematical Sciences, National Academy of Sciences. He is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association. He has served as Chair of the ASA Committee on Privacy and Confidentiality and on the ASA Board of Directors and the ISI Council.

COURSE MATERIALS
Registrants will be provided with a course lecture notebook.

MEALS
JPSM group continental breakfasts, lunches and refreshments are included in the course fee.

DAILY CHECK-IN
Course registrants should check-in with JPSM Onsite each day of the course.

TENTATIVE SCHEDULE