Data Shuffling via Quasi-Random Sampling
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Solution Overview
Problem
Current data security methods in cloud computing and data storage systems lack effective mechanisms for ensuring both data confidentiality and integrity, particularly in anonymizing and securely storing sensitive information.
Innovation Solution
A data shuffling and encryption system utilizing multi-dimensional quasi-random sampling for key-based data shuffling and encryption, which dynamically configures parameters for anonymity and security, ensuring that consecutive elements in the input data sequence are not next to each other in the output sequence, thereby providing both shuffling and encryption in a single process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in secure data storage with anonymization, then data confidentiality is improved, but data integrity verification becomes more difficult
Solution Approach 1:
The system pre-computes tokens using Sobol Sequence before data storage, creating verification credentials in advance. This preliminary action allows integrity verification to be performed efficiently later without adding complex real-time processing mechanisms to the secure storage system.
Solution Approach 2:
The patent introduces tokens as an intermediary element that mediates between the anonymized data and the verification process. These tokens contain encoded information that enables integrity verification without requiring access to the actual anonymized data, thus maintaining confidentiality while enabling verification.
2Reliability
If conventional encryption methods are used, then data confidentiality is improved, but data utility for analysis is worsened
Solution Approach 1:
The system applies different transformation qualities to different parts of the data. Sensitive fields undergo strong anonymization with tokens, while less sensitive fields maintain more of their original characteristics. This local differentiation allows analysis on non-sensitive data while protecting sensitive information.
Solution Approach 2:
The patent employs configurable anonymization parameters that can be adjusted based on the specific data field and analysis requirements. By changing parameters such as tokenization strength and generalization levels, the system balances confidentiality protection with data utility for different analytical purposes.
3Reliability
If strong anonymization is applied to protect confidentiality, then data security is improved, but the ability to verify data integrity is worsened
Solution Approach 1:
The system creates token copies that contain verified integrity information. These token copies serve as reference points that can be compared against the actual data to verify integrity without exposing the underlying sensitive information. The copying mechanism preserves verification capability while maintaining strong anonymization.
Data Source
Figure 1A~1B
Figure 1C
Figure 1D
AI summary
A data shuffling and encryption system may include data storage to store a key and a coding module to shuffle and encrypt an input data sequence. The shuffling and encrypting may include determining an n-dimensional space and populating the n-dimensional space with the input data sequence. A quasi-random function may be applied to sample the n-dimensional space, and samples may be used as an index into the n-dimensional space to determine an output data sequence from the n-dimensional space.