Anonymization Module for Cloud Data Security
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Solution Overview
Problem
As more enterprises use third-party hosted applications, there is a need for efficient data security measures to protect data from unauthorized access, particularly in cloud computing environments where data is transmitted and stored across different locations, and regulatory compliance is a concern.
Innovation Solution
A method and system for anonymizing data transmitted to and de-anonymizing data retrieved from a destination computing device, utilizing an anonymization strategy module that selects and applies anonymization techniques such as tokenization and symmetric key encryption to ensure data security, while maintaining search and sort capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is transmitted in plain text to third-party hosted applications, then data transmission is simple and fast, but data security and protection from unauthorized access deteriorates
Solution Approach 1:
The system performs data anonymization before transmission to the third-party hosted application. Sensitive fields are identified and replaced with anonymized values (such as masking credit card numbers or replacing social security numbers with placeholders) prior to transmission, ensuring that even if data is intercepted during transmission, the sensitive information is already protected.
Solution Approach 2:
The system introduces an anonymization layer as an intermediary between the client application and the third-party hosted application. This intermediary component selectively anonymizes sensitive data fields before transmission and can perform de-anonymization on retrieved data, acting as a security buffer that protects against unauthorized access while maintaining functional data exchange.
2Object-affected harmful factors
If data is anonymized before transmission, then data security is improved, but data processing complexity increases
Solution Approach 1:
The anonymization system segments the data structure by identifying and processing only specific sensitive fields separately from non-sensitive fields. The system analyzes the data schema to determine which fields require anonymization (such as credit card numbers, social security numbers, dates of birth) and applies anonymization only to those fields, leaving other data intact and simplifying the overall process.
Solution Approach 2:
The system changes the parameter representation of sensitive data fields by transforming them into anonymized equivalents. Instead of storing or transmitting actual sensitive values, the system uses anonymized parameters (such as masked patterns, hashed values, or placeholder tokens) that maintain the structural integrity of the data while eliminating sensitive information, thereby reducing complexity in data handling.
3Object-affected harmful factors
If all data fields are anonymized, then data security is maximized, but data usability and search capabilities deteriorate
Solution Approach 1:
The system applies different quality levels of anonymization to different data fields based on their sensitivity and functional requirements. Critical sensitive fields (such as credit card numbers and social security numbers) are fully anonymized, while less sensitive fields (such as names, addresses, or email addresses) may be partially anonymized or left unchanged, allowing the system to maintain usability where needed while maximizing security where critical.
Solution Approach 2:
The anonymization system dynamically adjusts the level of anonymization based on the operational context. When data is being transmitted or stored, maximum anonymization is applied for security. When data is being retrieved and processed locally, the system can dynamically de-anonymize the data to restore usability and search capabilities, allowing the same data structure to serve both security and functionality requirements at different times.
Data Source
AI summary
A method and system for anonymizing data to be transmitted to a destination computing device is disclosed. Data to be transmitted is received from a user computer. The data includes a plurality of fields of data. One or more fields of data are selected for anonymization. The selected one or more fields are anonymized. The data with one or more fields anonymized is transmitted to the destination computing device.