David Alexander Reizes

Title: David Alexander Reizes: Innovator in Content Sensitivity Analysis

Introduction

David Alexander Reizes is a notable inventor based in Menlo Park, CA. He has made significant contributions to the field of content sensitivity analysis, holding a total of 3 patents. His work focuses on enhancing the classification of monitored content, particularly in distinguishing between public and private data.

Latest Patents

Reizes' latest patents include innovative methods for partial document content matching using sectional analysis. This patent describes a system where monitored content is classified to determine partial matches with fragments of documents. A set of redundant keys, or sliding sectional fingerprints, are computed for every possible alignment of the documents with respect to the monitored content. The keys are stored in repositories according to the classification of the corresponding documents. Sectional fingerprints are computed for the monitored content, and the repositories are searched. If a match is found in a repository corresponding to public content, then the monitored data section is classified as public. If a match is found only in a repository corresponding to private content, then the data section is classified as private. Otherwise, the data section is classified as unknown. In a related aspect, a set of policies are searched for a first match in part according to the classifications of the monitored data sections, and a designated action is taken if the first match is found.

Another significant patent by Reizes is focused on inferring content sensitivity from partial content matching. This invention analyzes monitored content to determine full and partial matches to previously classified content. Monitored content matching previously classified public content is classified as public, even if the monitored content is also found to match previously classified private content. In other words, public classification 'overrides' potentially private classification. Monitored content matching only previously classified private content is classified as private. All remaining otherwise unclassified monitored content is classified as unknown. Monitored content is analyzed with respect to a session. If any content in a session is private, then the session is classified as private. If all content in a session is public, then the session is classified as public. Otherwise, the session is classified as unknown. In a related aspect, a set of policies are searched for a first match in part according to the classification, and a designated action is taken if the first match is found.

Career Highlights

Throughout his career, Reizes has worked with prominent companies such as RSA Security Inc. and EMC