Statistical Methods for Protecting Personally Identifiable Information in Aggregate Reporting

• Activity Number: 606
• Type: Topic Contributed
• Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Section on Government Statistics
• Author(s): Marilyn Seastrom*+
• Companies: National Center for Education Statistics
• Keywords: Disclosure Limitation Methods ; Confidentiality ; Privacy ; Aggregated Data ; Data Protection

Abstract

Over the last decade, there has been an expansion in the amount of information on students and their schools reported to the general public. States now report student outcomes based on assessments of student achievement in specific subjects and grade levels. These reports have the challenge of meeting reporting requirements while also meeting legal requirements to protect each student's personally identifiable information (PII). States have adopted minimum group size reporting rules and various forms of suppression and top/bottom coding to protect PII. This paper provides a review and analysis of current practices to illustrate that some practices work better than others in protecting PII reported from student education records. This review has also led to the formulation of recommended reporting rules that are driven by the size of the reporting groups or subgroups. These reporting rules are intended to maximize the amount of detail that can be safely reported without allowing disclosures from student outcome measure categories that are based on small numbers of students.