scholarly journals Low-Overhead Implementation of a Soft Decision Helper Data Algorithm for SRAM PUFs

2009 ◽  
pp. 332-347 ◽  
Author(s):  
Roel Maes ◽  
Pim Tuyls ◽  
Ingrid Verbauwhede
Keyword(s):  
Soft Decision ◽  
Helper Data

scholarly journals Multiple Observations for Secret-Key Binding with SRAM PUFs

2021 ◽  
Author(s):  
Lieneke Kusters ◽  
Frans M.J. Willems
Keyword(s):  
Error Correcting Code ◽  
Ldpc Code ◽  
Soft Decision ◽  
Secret Key ◽  
Multiple Observations ◽  
Reconstruction Performance ◽  
New Strategy ◽  
The One ◽  
Helper Data ◽  
Data Scheme

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.

scholarly journals Multiple Observations for Secret-Key Binding with SRAM PUFs

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 590
Author(s):  
Lieneke Kusters ◽  
Frans M. J. Willems
Keyword(s):  
Error Correcting Code ◽  
Ldpc Code ◽  
Soft Decision ◽  
Secret Key ◽  
Multiple Observations ◽  
Reconstruction Performance ◽  
New Strategy ◽  
The One ◽  
Helper Data ◽  
Data Scheme

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM-PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM-PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore, it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.

scholarly journals An Improved Fuzzy Vector Signature with Reusability

2020 ◽  
Vol 10 (20) ◽  
pp. 7141
Author(s):  
Ilhwan Lim ◽  
Minhye Seo ◽  
Dong Hoon Lee ◽  
Jong Hwan Park
Keyword(s):  
Sampling Method ◽  
Performance Comparison ◽  
Error Rates ◽  
Efficiency Improvement ◽  
Fuzzy Extractor ◽  
Authentication Protocols ◽  
Biometric Data ◽  
Single User ◽  
Helper Data ◽  
Fuzzy Vector

Fuzzy vector signature (FVS) is a new primitive where a fuzzy (biometric) data w is used to generate a verification key (VKw), and, later, a distinct fuzzy (biometric) data w′ (as well as a message) is used to generate a signature (σw′). The primary feature of FVS is that the signature (σw′) can be verified under the verification key (VKw) only if w is close to w′ in a certain predefined distance. Recently, Seo et al. proposed an FVS scheme that was constructed (loosely) using a subset-based sampling method to reduce the size of helper data. However, their construction fails to provide the reusability property that requires that no adversary gains the information on fuzzy (biometric) data even if multiple verification keys and relevant signatures of a single user, which are all generated with correlated fuzzy (biometric) data, are exposed to the adversary. In this paper, we propose an improved FVS scheme which is proven to be reusable with respect to arbitrary correlated fuzzy (biometric) inputs. Our efficiency improvement is achieved by strictly applying the subset-based sampling method used before to build a fuzzy extractor by Canetti et al. and by slightly modifying the structure of the verification key. Our FVS scheme can still tolerate sub-linear error rates of input sources and also reduce the signing cost of a user by about half of the original FVS scheme. Finally, we present authentication protocols based on fuzzy extractor and FVS scheme and give performance comparison between them in terms of computation and transmission costs.

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