Data Anonymization Module for Cloud 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 networks.
Innovation Solution
A method and system for anonymizing data transmitted to a destination computing device using an anonymization module that selectively extracts fields, computes hashes, and applies anonymization strategies, including tokenization and symmetric key encryption, to ensure secure data transmission and de-anonymization upon retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transmitted in plain text to third-party hosted applications, then data transmission is simple and fast, but data security and confidentiality are compromised
Solution Approach 1:
The system performs preliminary anonymization actions by extracting specific fields, computing hashes, and applying anonymization strategies (tokenization, encryption) before data transmission. This prepares the data in advance to ensure security without affecting the transmission process itself.
Solution Approach 2:
The anonymization module acts as an intermediary between the data source and the third-party application. It intercepts the data, applies security transformations, and releases the anonymized data, thereby mediating the transmission to maintain both simplicity and security.
2Reliability
If all data fields are anonymized, then data confidentiality is maximized, but data usability and search capability are reduced
Solution Approach 1:
The system applies different anonymization qualities to different data fields. Sensitive fields (PII) undergo full anonymization (tokenization/encryption), while non-sensitive fields remain unchanged or undergo minimal transformation. This allows the system to maintain confidentiality where needed while preserving usability where appropriate.
Solution Approach 2:
The anonymization process segments data fields into sensitive and non-sensitive categories. By selectively anonymizing only the necessary fields (name, email, phone) while leaving other fields intact, the system maintains a balance between security and functionality for search and sort operations.
3Reliability
If data is anonymized using complex transformations, then data security is enhanced, but processing time and computational resources increase
Solution Approach 1:
The system applies partial anonymization by selectively transforming only the necessary fields rather than all data. This partial action approach reduces computational overhead while maintaining adequate security for the critical sensitive information that requires protection.
Solution Approach 2:
The system changes parameters such as the level of anonymization, hash algorithm selection, and tokenization strategy based on the data type and sensitivity level. This allows optimization of processing time by using lighter transformations for less critical data while reserving more computationally intensive methods for highly sensitive fields.
Data Source
AI summary
A method and system for anonymizing data to be transmitted to a destination computing device is disclosed. Anonymization strategy for data anonymization is provided. Data to be transmitted is received from a user computer. The data includes a plurality of fields. One or more fields are selectively extracted. A hash using the extracted fields is computed. The computed hash is stored. Anonymization of the data is performed, using an anonymization module. Anonymized data is transmitted to the destination computing device over a network.