Data Classification via Feature Extraction

The patent introduces an intermediary representation layer between the original data and the classification system. Instead of storing and processing raw data directly, the system creates intermediate feature representations (such as hashed values, extracted characteristics, or transformed data) that preserve classification capability while eliminating direct data exposure. This intermediary layer acts as a mediator that allows the classification system to operate on data without accessing the actual sensitive information, thereby resolving the contradiction between data accessibility and privacy protection.

The patent extracts only the necessary information from the original data for classification purposes, separating the classification function from the data storage function. By extracting features, patterns, or representations of the data rather than storing the data itself, the system maintains classification productivity while eliminating the security risk associated with storing data copies. The extraction process creates a minimal representation that suffices for classification without exposing the full data to potential threats.

The patent transforms the data from its original form into a different parameter representation that maintains classification accuracy while improving security. This could involve changing data from raw format to encoded format, from full content to feature vectors, or from unhashed to hashed representation. The parameter transformation preserves the essential characteristics needed for accurate data type identification while removing or obfuscating the actual data content, thereby achieving both measurement precision and privacy protection.