Statistically Coherent Garbage Values for Ciphertext Pattern Resistance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing encryption methods are vulnerable to decryption attacks due to the distinguishable nature of garbage values in ciphertext, allowing attackers to identify and exploit patterns.
Innovation Solution
Blending pseudo-randomly generated garbage values with statistical patterns resembling target data before and after encryption, using Pseudo Random Numbers (PRNs) to make the garbage values indistinguishable from the target data, thereby impeding patternicity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encryption methods are used, then the encryption process is simple and fast, but the ciphertext is vulnerable to patternicity analysis attacks
Solution Approach 1:
The patent applies preliminary action by generating and blending garbage values into the plaintext data before the encryption process occurs. This pre-processing step modifies the input data to contain statistically coherent garbage values that follow the same distribution patterns as the original data, thereby strengthening the encryption against patternicity analysis while maintaining a relatively simple overall process structure
Solution Approach 2:
The patent changes the statistical parameters of the data by introducing garbage values with specific distribution characteristics. The garbage values are generated to match the statistical properties (mean, variance, distribution shape) of the original plaintext, transforming the data parameters in a way that preserves statistical coherence while enhancing security against statistical analysis attacks
2Ease of manufacture
If garbage values with uniform distribution are used, then the generation process is simple, but the garbage values are distinguishable from target data
Solution Approach 1:
The patent transforms the parameter distribution of garbage values from a simple uniform distribution to a distribution that matches the statistical characteristics of the target data. By adjusting parameters such as mean, variance, and distribution shape to align with the target data's statistical profile, the garbage values become indistinguishable from genuine data points while maintaining a relatively straightforward generation process
Solution Approach 2:
The patent copies the statistical distribution characteristics of the target data and applies them to the garbage value generation process. Instead of creating entirely new distribution patterns, the method replicates the existing statistical properties of the target data in the garbage values, making them statistically indistinguishable from the original data while keeping the generation process manageable
3Difficulty of detecting and measuring
If statistical pattern matching is used to generate garbage values, then the garbage values become indistinguishable from target data, but the generation process becomes more complex
Solution Approach 1:
The patent focuses on changing key statistical parameters (mean, variance, distribution type) rather than performing complex pattern matching across the entire data structure. By targeting specific statistical moments and distribution characteristics, the method achieves statistical coherence with the target data while avoiding the computational complexity of full pattern matching algorithms
Data Source
AI summary
A computer-implemented method includes identifying a multiplicity-bit size, constructing a multiplicity of independent Value Ranges, identifying an individual multiplicity-bit value as an initial first multiplicity-bit value, dividing each constructed Value Range into a multiplicity of contiguous Value-Range Bands that do not overlap and collectively encompass all possible values within the Value Range, using PRN values that range between the highest and lowest value within the Value Range associated with the first multiplicity-bit value, to identify a second multiplicity-bit value associated with the Value-Range Band the PRN falls within, concatenating the identified second multiplicity bit value to the first multiplicity-bit value to create a concatenated bit sequence of multiplicity-bit values, designating the second multiplicity-bit value a first bit value, and repeating the second multiplicity-bit identification sequence, concatenating all identified second multiplicity-bit values to the existing concatenated bit sequence of identified second multiplicity-bit values.