Efficient Fuzzy Extraction of PUF-Induced Secrets: Theory and Applications

Delvaux, Jeroen; Gu, Dawu; Verbauwhede, Ingrid; Hiller, Matthias; Yu, Meng-Day

doi:10.1007/978-3-662-53140-2_20

Cited by 67 publications

(55 citation statements)

References 28 publications

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“…This is mainly caused by the (n − k)-bit helper data exposure. Extra entropy leakage can be from response bias [30], [30], [31]; a bias exists when the probability of a response being '1' or '0' is not ideally 50%. Active helper data manipulation (HDM) attacks may also cause serious entropy leakage if the helper data algorithm is not carefully constructed [23], [27], [32], [33].…”

Section: Security Discussionmentioning

confidence: 99%

“…The reverse fuzzy extractor can result in unanticipated entropy loss under repeated helper data exposure associated with a given PUF response r ; unless, PUF responses are unbiased. Generally, the extra entropy loss is a result of the leakage of bit-specific reliability information [31]. However, the above extra entropy losses are important only when PUF response bias is considerably different from the ideal value of 50% as shown by the analysis in [18], [31].…”

Section: Security Discussionmentioning

confidence: 99%

“…Generally, the extra entropy loss is a result of the leakage of bit-specific reliability information [31]. However, the above extra entropy losses are important only when PUF response bias is considerably different from the ideal value of 50% as shown by the analysis in [18], [31]. In practice, modern silicon PUFs usually have a good bias performanceclose to 50% [3].…”

Section: Security Discussionmentioning

confidence: 99%

“…As a result, debiasing the biased raw responses [30] must be undertaken first, e.g., via classic von Neumann (CVN) debias-ing, pair-output von Neumann debiasing with erasures ( -2O-VN) and Hamming Weight (HW) based de-biasing [22]-an example of employing a HW based debiasing method for key derivation with a resource limited devices is detailed in [22]. Notably, not all debiasing schemes offer reusability-multiple use of the same PUF response-for a reverse fuzzy extractor [31]. Thus, debiasing schemes, e.g., -2O-VN, that offer reusability should be chosen for the reverse fuzzy extractor.…”

Section: Security Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Building Secure SRAM PUF Key Generators on Resource Constrained Devices

Gao

Yang

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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A securely maintained key is the premise upon which data stored and transmitted by ubiquitously deployed resource limited devices, such as those in the Internet of Things (IoT), are protected. However, many of these devices lack a secure non-volatile memory (NVM) for storing keys because of cost constraints. Silicon physical unclonable functions (PUFs) offering unique device specific secrets to electronic commodities are a low-cost alternative to secure NVM. As a physical hardware security primitive, reliability of a PUF is affected by thermal noise and changes in environmental conditions; consequently, PUF responses cannot be directly employed as cryptographic keys. A fuzzy extractor can turn noisy PUF responses into usable cryptographic keys. However, a fuzzy extractor is not immediately mountable on (highly) resource constrained devices due to its implementation overhead. We present a methodology for constructing a lightweight and secure PUF key generator for resource limited devices. In particular, we focus on PUFs constructed from pervasively embedded SRAM in modern microcontroller units and use a batteryless computational radio frequency identification (CRFID) device as a representative resource constrained IoT device in a case study.

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Section: Security Discussionmentioning

confidence: 99%

Section: Security Discussionmentioning

confidence: 99%

Section: Security Discussionmentioning

confidence: 99%

Section: Security Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Building Secure SRAM PUF Key Generators on Resource Constrained Devices

Gao

Yang

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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“…Tbl. I, because n − k is only an upper bound for the entropy loss that holds with equality only if m = n [11].…”

Section: B (N-k) Boundmentioning

confidence: 99%

Efficient Bound for Conditional Min-Entropy of Physical Unclonable Functions Beyond IID

Wilde

Frisch

Pehl

2019

2019 IEEE International Workshop on Information Forensics and Security (WIFS)

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The remaining min-entropy of a secret generated by fuzzy extraction from a Physical Unclonable Function is typically estimated under the assumption of independent and identically distributed PUF responses, but this assumption does not hold in practice. This work analyzes the more realistic case that the responses are independent but not necessarily identically distributed. For this case, we extend the (n-k) bound and a tighter bound by Delvaux et al. In particular, we suggest a grouping bound which provides a trade off for accuracy vs computational effort. Comparison to previous bounds shows the accuracy and efficiency of our bound. We also adapt the key rank (a tool from side-channel analysis) to cross-validate the state-of-the-art and our proposed min-entropy bounds based on publicly available PUF data from real hardware.

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Application of Resistive Random Access Memory in Hardware Security: A Review

Rajendran

Banerjee

Chattopadhyay

et al. 2021

Adv Elect Materials

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today, with the low power embedded devices deployed for sensing, actuating and running intelligent decision-making algorithms. The advancement in wireless communication technologies such as WiFi, Bluetooth low energy, 4G-LTE, LoRaWAN and recently 5G millimeter wave communication coupled with huge boost in the computing capabilities enable these devices to exchange and process data both at the edge and cloud. This intelligent environment is now widely explored as the Internet of Things (IoT). One of the primary concerns in this development is to security. Most of the available protocols and encryption techniques for device authentication and data transfer are purely software measures, thus presenting an opportunity for a determined attacker to bypass those with higher computing power or uncover the secrets through information leakages in physical forms. Furthermore, software-driven security demands high system resources, which is unavailable in resource-constrained IoT devices, therefore necessitating robust hardware security solutions that can be integrated into those devices. On the other hand, globalization of the semiconductor industry supply chain mandates a careful auditing and trust management during the fabrication and system integration process, which can very well benefit from the inherent randomness in the manufacturing processes. Hence, hardware security research today predominantly focuses on designing security primitives that tap onto the manufacturing variations, thereby constructing primitives like true random number generator (TRNG), physical unclonable function (PUF), and cryptographic hash functions.The emerging nonvolatile memory (NVM) devices such as resistive random access memory (RRAM), spin-transfer torque magnetic random-access memory (STT-MRAM), spin-orbit torque magnetic random-access memory (SOT-MRAM), and ferroelectric field-effect transistors (FeFET) are currently explored for building beyond Von Neumann architectures. Among them, RRAM is well established today. It is a two-terminal device commonly known for its metal-insulator-metal structure. This device technology's strength is the available wide range of functional materials that can be engineered for reliable resistive switching. It includes binary transition metal oxides, 2D materials likeNowadays, advancements in the design of trusted system environments are relying on security provided by hardware-based primitives, while replacing resource-hungry software security measures. Emerging nonvolatile memory devices are promising candidates to provide the required hardware security functionalities at very low area-energy-runtime budget. Resistive random access memory (RRAM) offers high-density integration with outstanding performance among the state-of-the-art nonvolatile memory devices. The RRAM device technology is currently getting significant attention from both academia and industry for constructing beyond Von Neumann architectures. This technology's strength is its scalable two-terminal structure, the availability of wide range...

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Efficient Fuzzy Extraction of PUF-Induced Secrets: Theory and Applications

Cited by 67 publications

References 28 publications

Building Secure SRAM PUF Key Generators on Resource Constrained Devices

Building Secure SRAM PUF Key Generators on Resource Constrained Devices

Efficient Bound for Conditional Min-Entropy of Physical Unclonable Functions Beyond IID

Application of Resistive Random Access Memory in Hardware Security: A Review

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