PUF Response Concatenation for Device Authentication Error Rate Control
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Solution Overview
Problem
Existing device authentication technologies using Physically Unclonable Functions (PUFs) face challenges in controlling the error rate of device-specific information, which affects circuit scale and data size, making it difficult to meet security requirements for various applications without increasing manufacturing costs.
Innovation Solution
A method that involves inputting data multiple times into a PUF circuit, processing responses to create subsets, concatenating them, and adjusting parameters to control the error rate by generating reference and synthesized data, ensuring the error rate falls within a preset range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the error rate is reduced to meet high security requirements, then the security level is improved, but the circuit scale and data size increase
Solution Approach 1:
The patent segments the response data into multiple subsets and processes them separately through repeated input operations. By dividing the data processing into discrete segments (subsets) that can be independently handled, the system achieves lower error rates without requiring a proportional increase in overall circuit scale, as each segment is processed efficiently through the existing PUF circuit infrastructure.
Solution Approach 2:
The patent performs preliminary processing of response data by creating multiple subsets and repeating input operations before final authentication decisions are made. This preliminary action of pre-processing and pre-computing with multiple repetitions allows the system to reduce error rates in advance, avoiding the need for larger circuits during the actual authentication operation.
2Reliability
If the error rate is reduced to meet high security requirements, then the security level is improved, but the data size increases
Solution Approach 1:
The patent segments response data into multiple subsets, allowing the system to work with smaller data portions at each processing step rather than requiring all data to be stored simultaneously. This segmentation enables error reduction through repeated processing of smaller subsets, avoiding the need to increase overall data storage size proportionally.
Solution Approach 2:
The patent employs periodic repeated input operations on the PUF circuit with the same challenge, processing responses in cycles. This periodic action allows error reduction through multiple measurements and comparisons, achieving high reliability without requiring proportionally larger data storage, as the same data is reused across multiple processing cycles.
3Adaptability or versatility
If the PUF is manufactured to meet different target error rates for various applications, then the adaptability to different applications is improved, but the manufacturing cost increases
Solution Approach 1:
The patent implements a universal error rate control mechanism that can be applied to PUF circuits regardless of the specific application. The same core methodology of repeated input operations, subset creation, and error rate calculation can serve multiple applications with different security requirements, eliminating the need for separate manufacturing processes for each application and thereby reducing manufacturing costs.
Solution Approach 2:
The patent enables different error rates to be achieved by changing operational parameters (number of repetitions, subset sizes, threshold values) rather than changing the physical PUF circuit itself. This parameter-based adjustment allows the same manufactured PUF to be adapted to different applications' security requirements, avoiding the need for costly re-manufacturing for each application.
Data Source
AI summary
A method and a program capable of controlling an error rate of device-specific information are provided. Provided is the method for controlling an error rate of device-specific information, including a step S1 of: inputting each of i (i is an arbitrary natural number) challenges, j times (j is an arbitrary natural number), into a PUF mounted chip; leaving j responses intact (j′=j) or processing j responses into j′ pieces (0<j′<j); and registering them in the database beforehand in association with each piece of the input data, a step S2 of inputting i challenges into the database, a step S3 of: reading j′ responses corresponding to the respective i challenges from the database; concatenating the j′ responses for each piece of the input data; further concatenating the concatenated data by k′ repetitions (0<k′≤k, and k is an arbitrary natural number, but is a natural number of 2 or more if the i and the j are both 1); obtaining the concatenated (j′×k′) responses for each piece of the input data; and further concatenating them also for different input data to obtain concatenated (i×j′×k′) responses and thereby generate reference data, a step S4 of: inputting i challenges, k times, for each challenge into the PUF mounted chip; leaving obtained k responses intact as k′=k or processing the obtained k responses into k′ pieces (0<k′<k); concatenating obtained k′ responses by j′ repetitions for each response; further concatenating them for all of the k′ responses; further concatenating concatenated (j′×k′) responses also for different input data; and obtaining concatenated (i×j′×k′) responses to generate synthesized output data, and a step S5 of deciding whether or not the synthesized output data matches the reference data (specifically, whether a Hamming distance between both data is a threshold value or less), and the method determines whether or not the error rate of the synthesized output data is within a preset range based on the decision result in step S5, and changes at least one of i, j, j′, k, and k′ to repeat steps S1 to S5 until the error rate falls within the preset range if the error rate is determined not to be within the preset range.