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1. Upgrading Fuzzy Extractors

   https://doi.org/10.1007/978-3-031-54770-6_7  

   Cachet, Chloe; Hamlin, Ariel; Rezapour, Maryam; Fuller, Benjamin (March 2024, Applied Cryptography and Network Security)

   Pöpper, Christina; Batina, Lejla (Ed.)

   Fuzzy extractors derive stable keys from noisy sources non-interactively (Dodis et al., SIAM Journal of Computing 2008). Since their introduction, research has focused on two tasks: 1) showing security for as many distributions as possible and 2) providing stronger security guarantees including allowing one to enroll the same value multiple times (reusability), security against an active attacker (robustness), and preventing leakage about the enrolled value (privacy). Given the need for progress on the basic fuzzy extractor primitive, it is prudent to seek generic mechanisms to transform a fuzzy extractor into one that is robust, private, and reusable so that it can inherit further improvements. This work asks if one can generically upgrade fuzzy extractors to achieve robustness, privacy, and reusability. We show positive and negative results: we show upgrades for robustness and privacy, but we provide a negative result on reuse. 1. We upgrade (private) fuzzy extractors to be robust under weaker assumptions than previously known in the common reference string model. 2. We show a generic upgrade for a private fuzzy extractor using multi-bit compute and compare (MBCC) obfuscation (Wichs and Zirdelis, FOCS 2017) that requires less entropy than prior work. 3. We show one cannot arbitrarily compose private fuzzy extractors. In particular, we show that assuming MBCC obfuscation and collision-resistant hash functions, there does not exist a private fuzzy extractor secure against unpredictable auxiliary inputs, strengthening a negative result of Brzuska et al. (Crypto 2014). 

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2. Computational fuzzy extractors

   https://doi.org/10.1016/j.ic.2020.104602  

   Fuller, Benjamin; Meng, Xianrui; Reyzin, Leonid (December 2020, Information and Computation)

   null (Ed.)
3. Fuzzy classification of gear fault using principal component analysis-based fuzzy neural network

   https://doi.org/10.1115/ISFA2020-9632  

   Zhou, K. (September 2020, Proceedings of the ASME 2020 Internal International Symposium on Flexible Automation)

   null (Ed.)

   Condition assessment of machinery components such as gears is important to maintain their normal operations and thus can bring benefit to their life circle management. Data-driven approaches haven been a promising way for such gear condition monitoring and fault diagnosis. In practical situation, gears generally have a variety of fault types, some of which exhibit continuous severities of fault. Vibration data collected oftentimes are limited to reflect all possible fault types. Therefore, there is practical need to utilize the data with a few discrete fault severities in training and then infer fault severities for the general scenario. To achieve this, we develop a fuzzy neural network (FNN) model to classify the continuous severities of gear faults based on the experimental measurement. Principal component analysis (PCA) is integrated with the FNN model to capture the main features of the time-series vibration signals with dimensional reduction for the sake of computational efficiency. Systematic case studies are carried out to validate the effectiveness of proposed methodology. 

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4. When Are Fuzzy Extractors Possible?

   https://doi.org/10.1109/TIT.2020.2984751  

   Fuller, Benjamin; Reyzin, Leonid; Smith, Adam (August 2020, IEEE Transactions on Information Theory)

   null (Ed.)
5. Fuzzy Clustering with Similarity Queries

   Huleihel, Wasim; Mazumdar, Arya; Pal, Soumyabrata (January 2021, NeurIPS 2021)