Post-Quantum Cryptography Risk Profile Generation
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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 threat to data security even before quantum computing capabilities are fully realized, necessitating a migration to quantum-resistant algorithms.
Innovation Solution
The implementation of post-quantum cryptography (PQC) systems that generate a risk profile data structure to determine the appropriate PQC cryptographic technique for encrypting data, utilizing techniques like hash-based, lattice-based, isogeny-based, code-based, and zero-knowledge proof methods to secure data against quantum attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical cryptographic schemes (RSA, Diffie-Hellman) are used, then current computational security is maintained, but vulnerability to quantum computer attacks increases
Solution Approach 1:
The patent changes the fundamental cryptographic parameters by transitioning from classical algorithms (RSA, Diffie-Hellman) to post-quantum algorithms (lattice-based, code-based, multivariate, hash-based, isogeny-based cryptography). This parameter change maintains security reliability against quantum attacks while managing system complexity through structured implementation frameworks.
Solution Approach 2:
The patent segments the cryptographic migration process into distinct phases: risk assessment, algorithm selection, implementation, and validation. By dividing the complex migration task into manageable segments, the system reduces overall complexity while ensuring comprehensive security against quantum threats.
2Reliability
If migration to quantum-resistant algorithms is implemented, then future quantum security is ensured, but current system compatibility and operational complexity increase
Solution Approach 1:
The patent performs preliminary risk assessment and algorithm selection before full migration is executed. By conducting advance analysis of data vulnerability and selecting appropriate post-quantum algorithms in advance, the system reduces operational complexity during the actual migration process while ensuring quantum resistance.
Solution Approach 2:
The patent introduces intermediary components including risk profile data structures, policy attribute generation mechanisms, and cryptographic technique selection frameworks. These intermediaries facilitate the transition from classical to quantum-resistant cryptography by providing structured decision-making processes that simplify migration operations.
3Reliability
If comprehensive data encryption with multiple PQC techniques is applied, then security coverage is improved, but computational overhead and processing time increase
Solution Approach 1:
The patent applies different post-quantum cryptographic techniques to different data types and risk profiles rather than uniformly encrypting all data. By matching specific PQC algorithms (lattice-based, code-based, multivariate, hash-based, or isogeny-based) to local data characteristics and vulnerability assessments, the system improves security coverage while minimizing unnecessary computational overhead.
Solution Approach 2:
The patent implements partial encryption strategies where only data meeting specific risk criteria undergoes post-quantum cryptographic processing. By applying encryption selectively rather than universally, the system achieves adequate security coverage while maintaining acceptable data processing speeds for low-risk data.
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 retrieving policy information associated with the data. The example method further includes generating a set of policy attributes about the data based on the data and the policy information. Subsequently, the example method includes generating a risk profile data structure based on the set of policy attributes. The risk profile data structure may be indicative of a vulnerability of the data in a PQC data environment.