DRBG Statistical Object Generator for Secure Initialization
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Solution Overview
Problem
Existing methods for generating statistical objects in cybersecurity are inefficient and insecure, requiring constant presence of original object material for hashing systems, which complicates secure handling and increases exposure of initialization vectors.
Innovation Solution
The use of Deterministic Random Bit Generator (DRBG) Statistical Object Generators, which maintain an internal state and require the initialization vector only during initialization, allowing for secure and efficient generation of statistical objects through a chain of DRBG generators, reducing exposure and enhancing computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hashing systems are used to generate statistical objects, then statistical objects can be generated, but the original object material must be constantly present which complicates secure handling and increases exposure of initialization vectors
Solution Approach 1:
The patent applies preliminary action by pre-initializing the DRBG with the initialization vector (IV) before statistical object generation. The IV is loaded into the DRBG's internal state during initialization, and subsequent generation operations only require generation inputs, not the original object material. This resolves the contradiction by performing the security-sensitive initialization operation once in advance, eliminating the need for constant presence of original material during generation operations.
2Productivity
If hashing systems are used to generate statistical objects, then statistical objects can be generated, but constant presence of original object material is required which increases exposure of initialization vectors
Solution Approach 1:
The patent extracts the initialization vector from the generation process by loading it into the DRBG's internal state during initialization. The IV is taken out of the continuous generation input requirements and stored securely within the generator's state. This resolves the contradiction by separating the IV from the ongoing generation operations, allowing efficient generation without re-exposing the IV.
3Reliability
If DRBG Statistical Object Generators are chained together, then security is increased, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the statistical object generation system into multiple independent DRBG generators chained together. Each DRBG in the chain operates independently with its own internal state and generation process. This resolves the contradiction by segmenting the security function across multiple units, where each segment contributes to overall security while maintaining independent, manageable complexity within each segment.
Solution Approach 2:
The patent implements nesting by chaining DRBG generators where the output of one DRBG serves as input to the next DRBG in the sequence. This nested arrangement allows multiple layers of deterministic random bit generation to be composed together, enhancing security through multiple transformation stages while organizing the complexity in a structured, hierarchical manner.
Data Source
AI summary
The present invention provides methods and apparatus to generate a statistical object, the deterministic statistical representation of an original object, using a Deterministic Random Bit Generator (DRBG) (10). Multiple DRBG Statistical Object Generators (10) may be chained together to increase security by using independent security configurations (22) for each DRBG Statistical Object Generator (10).