Service Request Data Anonymization With Consistent Token Replacement
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Solution Overview
Problem
Conventional data anonymization techniques fail to anonymize sensitive information in service request documents without destroying the internal structure or exposing customer networks to malicious threats, and are not scalable for diverse network, location, and user data.
Innovation Solution
An anonymization server employs a combination of rule-based and deep learning techniques, including data preprocessing, tagging, collision resolution, and replacement logic to identify and identically replace repeated sensitive information, preserving the internal consistency of the documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional data anonymization techniques are used, then sensitive information is anonymized, but the internal structure of the document is destroyed
Solution Approach 1:
The system segments the anonymization process into distinct stages: identifying repeated sensitive information sequences, tagging them with unique identifiers, and replacing them with anonymized tokens. This segmentation allows the system to selectively anonymize only the sensitive portions while leaving the rest of the document structure intact.
Solution Approach 2:
The system creates anonymized copies of the original document by replacing sensitive information sequences with standardized anonymized tokens. This copying approach preserves the document's internal structure and relationships while eliminating sensitive data, allowing the anonymized version to maintain usability for analysis purposes.
2Object-affected harmful factors
If conventional data anonymization techniques are used, then sensitive information is anonymized, but customer networks are exposed to malicious threats
Solution Approach 1:
The system performs preliminary anonymization of service request documents before they are stored or shared. By proactively identifying and anonymizing sensitive information sequences in advance, the system prevents potential security threats before they can materialize, ensuring that even if documents are accessed, the sensitive information is already protected.
3Device complexity
If conventional data anonymization techniques are used, then processing is simple, but scalability for diverse network, location, and user data is limited
Solution Approach 1:
The system employs a universal anonymization approach that identifies repeated information sequences regardless of their content or context. The same tagging and replacement mechanism works across diverse data types including network information, location data, and user information, making the system scalable and adaptable without requiring data-type-specific processing logic.
Data Source
AI summary
In one example embodiment, a server that is in communication with a network that includes a plurality of network elements obtains, from the network, a service request record that includes sensitive information related to at least one of the plurality of network elements. The server parses the service request record to determine that the service request record includes a sequence of characters that is repeated in the service request record, and tags the sequence of characters as a particular sensitive information type. Based on the tagging, the server identically replaces the sequence of characters so as to preserve an internal consistency of the service request record. After identically replacing the sequence of characters, the server publishes the service request record for analysis without revealing the sequence of characters.