PUF Identifier Generation Using Cell Subsets and Volatile Storage
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Solution Overview
Problem
Existing PUF implementations require a large amount of auxiliary data for error correction, which increases memory requirements and makes them vulnerable to attacks, as stable cells need to be stored and identified during the personalization phase.
Innovation Solution
Subdividing cells into subsets to determine reconstructable and error information items, allowing for the creation of an identifier without the need for storing masking data, thereby reducing auxiliary data requirements and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cells are classified and stable cells are selected during personalization phase, then the number of expected errors in raw data is reduced, but masking data must be stored in nonvolatile memory which increases memory requirements and enables attacks
Solution Approach 1:
The patent extracts only the essential stable cell information needed for error correction, removing the need to store complete masking data in nonvolatile memory. By taking out only the critical error correction information and discarding redundant masking data, the solution reduces auxiliary data storage while maintaining error correction capability.
Solution Approach 2:
The patent uses volatile memory instead of nonvolatile memory for storing auxiliary data, treating the storage as temporary and disposable. The auxiliary data in volatile memory can be cleared after use, eliminating the need for persistent nonvolatile storage and reducing the attack surface for long-term security threats.
2Reliability
If masking data is stored in nonvolatile memory for error correction, then error correction can be performed, but the PUF becomes vulnerable to attacks through masking data manipulation
Solution Approach 1:
The patent uses volatile memory for auxiliary data storage, which is temporary and can be cleared. This disposable approach to storage means the data exists only temporarily during operation and cannot be manipulated over the long term, eliminating the security vulnerability associated with persistent nonvolatile storage.
Solution Approach 2:
The patent introduces volatile memory as an intermediary between the PUF cells and the error correction process. This intermediary provides temporary storage that enables error correction while preventing direct access and manipulation of the core PUF data, thus maintaining security.
3Productivity
If all available PUF cells are used for data extraction, then the data rate is maximized, but unstable cells cause a large number of bit errors requiring powerful error correction
Solution Approach 1:
The patent segments the PUF cells into different groups based on their stability characteristics. By dividing the cell population into stable and unstable categories, the system can extract data from stable cells with high reliability while handling unstable cells separately, thus maintaining both high data rates and low error rates.
Solution Approach 2:
The patent applies different quality standards to different cells based on their individual stability properties. Stable cells are used for primary data extraction with high confidence, while unstable cells are treated differently or excluded, ensuring that each cell contributes to the data rate according to its local quality characteristics.
Data Source
AI summary
A method for determining an identifier on the basis of a multiplicity of cells is proposed, wherein the cells are subdivided into subsets, wherein the fact of whether a reconstructable information item is determinable is ascertained for each of the subsets, wherein, if a reconstructable information item is determinable for a subset, the reconstructable information item is determined and stored, wherein, if a reconstructable information item is not determinable for a subset, an error information item is determined and stored for this subset.