Post-Quantum Cryptography Optimization via ML
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Solution Overview
Problem
Current cryptographic systems, such as RSA and Diffie-Hellman, are vulnerable to quantum computers due to their reliance on mathematical problems that can be efficiently solved by quantum algorithms like Shor's and Grover's, posing a risk to data security even before quantum computers become capable, necessitating a migration to quantum-resistant algorithms.
Innovation Solution
A post-quantum cryptography (PQC) system that selects and implements quantum-resistant cryptographic techniques, including hash-based, lattice-based, isogeny-based, code-based, and zero-knowledge proof methods, using machine learning to optimize encryption based on data attributes and threat data structures, ensuring data security against quantum attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum-resistant cryptographic algorithms are implemented, then data security against quantum attacks is improved, but computational overhead and processing time increase
Solution Approach 1:
The system performs preliminary classification of data into security categories before encryption, selecting appropriate quantum-resistant algorithms in advance based on data sensitivity. This preliminary action allows the system to prepare encryption parameters ahead of time, reducing actual processing time when encryption is needed.
Solution Approach 2:
The system dynamically adjusts cryptographic parameters such as key lengths and algorithm complexity based on data classification levels. For less sensitive data, lighter quantum-resistant algorithms are used, while only highly sensitive data receives full-strength encryption, optimizing the balance between security and processing efficiency.
2Reliability
If quantum-resistant cryptographic algorithms are implemented, then resistance to quantum computing threats is improved, but system complexity increases
Solution Approach 1:
The cryptographic system is segmented into multiple independent modules, each handling specific quantum-resistant algorithms. The data classification module, algorithm selection module, and encryption module operate separately, making the complex system more manageable and maintainable while preserving quantum resistance.
Solution Approach 2:
An intermediary classification layer is introduced between data and encryption operations. This intermediary categorizes data and translates security requirements into appropriate cryptographic parameters, simplifying the interface between diverse quantum-resistant algorithms and application requirements.
3Adaptability or versatility
If comprehensive data classification is performed, then appropriate encryption selection is improved, but computational overhead increases
Solution Approach 1:
The system performs partial classification by focusing only on the most relevant data attributes for security categorization rather than analyzing all possible data characteristics. This selective approach provides sufficient encryption guidance while minimizing computational overhead from unnecessary analysis.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for post-quantum cryptography (PQC). An example method includes receiving data. The example method further includes receiving a set of data attributes about the data. The set of data attributes comprises one or more sets of data environment data attributes that are each representative of a set of data environments associated with the data. The example method further includes receiving one or more sets of data environment threat data structures associated with one or more data environments in the one or more sets of data environments associated with the data. The example method further includes selecting one or more cryptographic techniques for encrypting the data for at least the one or more data environments based on the set of data attributes, the one or more sets of data environment threat data structures, and a cryptograph optimization machine learning model.