Behavior-Driven Data Anonymization for Secure Leak Traceability
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Solution Overview
Problem
Existing anonymization methods in communication networks are insecure due to fixed encryption keys, making it easy for attackers to reverse-engineer anonymized data and violate user privacy.
Innovation Solution
Anonymization is performed using a dynamically generated permutation character sequence based on behavior data, including user identifiers and system information, with embedded watermark ciphertext sequences to enhance security and enable tracing of data leaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed encryption keys and permutation character sequences are used for anonymization, then the anonymization process is simple and efficient, but the anonymization security is poor and can be easily cracked by attackers
Solution Approach 1:
The patent applies dynamics by making the permutation character sequence variable rather than fixed. The sequence changes based on behavior data including user identifiers, system information, and random numbers, transforming the static anonymization process into a dynamic one that adapts to different access scenarios, thereby improving security without excessive complexity
Solution Approach 2:
The patent changes parameters by incorporating multiple variables into the permutation character sequence generation process. Instead of using a fixed sequence, the system varies the sequence based on user identifiers, system information, access time, and random numbers, making each anonymization operation unique and resistant to cracking
2Reliability
If fixed permutation character sequences are used, then the anonymization process is fast and efficient, but attackers can restore the original data once they figure out the encryption key
Solution Approach 1:
The patent applies preliminary action by pre-generating permutation character sequences based on behavior data before actual data access occurs. The system prepares the anonymization parameters in advance based on user identifiers and system information, so that when data access happens, the anonymization can be performed quickly with pre-computed sequences
Solution Approach 2:
The system changes parameters dynamically by incorporating random numbers and behavior-specific data into the permutation character sequence. This makes each anonymization operation unique while maintaining processing efficiency through algorithmic optimization
3Reliability
If behavior data is used to generate dynamic permutation character sequences, then anonymization security is improved, but the system complexity and computational overhead increase
Solution Approach 1:
The patent applies universality by designing a behavior data collection mechanism that serves multiple purposes: it tracks user access behavior for security auditing, provides input for dynamic permutation character sequence generation, and enables traceability of data access. This multi-functional approach adds security capabilities without requiring separate dedicated systems
Solution Approach 2:
The system uses behavior data as an intermediary between the user and the anonymization process. Instead of directly using fixed encryption keys, the behavior data mediates the generation of dynamic permutation character sequences, adding a layer of security while maintaining system manageability
Data Source
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AI summary
This disclosure provides an anonymization method and apparatus, a device, and a storage medium, and pertains to the field of communications network technologies. The method includes: receiving a data obtaining request of a first terminal, and obtaining requested target data based on the data obtaining request; determining behavior data generated when the target data is obtained; determining, based on the behavior data, a first permutation character sequence corresponding to the target data; and anonymizing, based on the first permutation character sequence, a to-be-anonymized character string in the target data, and outputting the anonymized target data. In this disclosure, because the behavior data is different each time and is not easy to crack, anonymization is implemented without relying on plaintext information, thereby improving anonymization security and meeting anonymization requirements specified by laws.