Dynamic Data Masking Server for Reverse Engineering Protection
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Solution Overview
Problem
Existing data masking methods fail to effectively protect sensitive information from reverse engineering, particularly in the context of large datasets like airline records, where maintaining data integrity and confidentiality is crucial.
Innovation Solution
A computer-implemented method and system that uses a data masking server to group digital records, generate random masking factors, and apply these factors to mask sensitive data within predetermined limits, ensuring that even if unauthorized access occurs, the underlying data remains secure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data masking methods are used, then data can be protected to some extent, but the data can still be reverse engineered with advanced computer technologies
Solution Approach 1:
The patent transforms the masking approach by changing from static masking values to dynamic masking factors that are randomly generated. Multiple sets of masking factors are created, each set containing numerous random numbers that can be applied to data values. This parameter change ensures that even if one set is compromised, many other sets remain secure, making reverse engineering impractical.
Solution Approach 2:
The system introduces dynamics by randomly selecting masking factors from multiple pre-generated sets rather than using fixed masking values. The random selection process ensures that the masking applied to data changes unpredictably, preventing attackers from establishing consistent patterns that could lead to reverse engineering.
2Object-affected harmful factors
If data is masked using random factors, then reverse engineering becomes difficult, but data integrity and statistical properties may be compromised
Solution Approach 1:
The patent carefully controls the parameters of the random masking factors to preserve statistical properties. By generating masking factors with specific distributions and constraints, the system maintains the overall statistical characteristics of the original data while still providing strong protection against reverse engineering.
Solution Approach 2:
The system creates multiple copies of masking factor sets with different random values. These copies allow the masked data to retain its statistical properties for legitimate analysis while ensuring that any single copy alone cannot be reverse engineered to reveal the original data.
3Reliability
If multiple sets of masking factors are generated and randomly selected, then data security is improved, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-generating multiple sets of masking factors before the actual data masking operation. This preparation phase allows the complex computation to be done in advance, so that during data processing, the system only needs to randomly select from the pre-generated sets, significantly reducing the computational burden during critical operations.
Solution Approach 2:
The masking system is segmented into distinct components: generation of masking factor sets, random selection of sets, and application of masking factors. This segmentation allows each component to be optimized independently and makes the overall system more manageable and efficient despite the complexity of using multiple masking sets.
Data Source
AI summary
Systems and methods of masking data are provided. A data masking server obtains a plurality of digital records and groups the plurality of digital records into a plurality of groups based on predetermined criteria. The data masking server generates a plurality sets of masking factors. Each set includes a predetermined number of random numbers as the masking factors. The data masking server generates a high limit value and a low limit value for a group of digital records. The data masking server randomly selects a set of masking factors. The data masking server randomly selects a random number from the randomly selected set of masking factors. The data masking server masks data contained in a digital record in the group of digital records based on the high limit value, the low limit value, and the randomly selected random number.