Fuzzy extractors derive strong keys from noisy sources. Their security is defined informationtheoretically, which limits the length of the derived key, sometimes making it too short to be useful. We ask whether it is possible to obtain longer keys by considering computational security, and show the following.• Negative Result: Noise tolerance in fuzzy extractors is usually achieved using an information reconciliation component called a "secure sketch." The security of this component, which directly affects the length of the resulting key, is subject to lower bounds from coding theory. We show that, even when defined computationally, secure sketches are still subject to lower bounds from coding theory. Specifically, we consider two computational relaxations of the information-theoretic security requirement of secure sketches, using conditional HILL entropy and unpredictability entropy. For both cases we show that computational secure sketches cannot outperform the best information-theoretic secure sketches in the case of high-entropy Hamming metric sources.• Positive Result: We show that the negative result can be overcome by analyzing computational fuzzy extractors directly. Namely, we show how to build a computational fuzzy extractor whose output key length equals the entropy of the source (this is impossible in the information-theoretic setting). Our construction is based on the hardness of the Learning with Errors (LWE) problem, and is secure when the noisy source is uniform or symbol-fixing (that is, each dimension is either uniform or fixed). As part of the security proof, we show a result of independent interest, namely that the decision version of LWE is secure even when a small number of dimensions has no error.

Abstract-Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies.However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases.At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions:1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search. Index Terms-searchable symmetric encryption, property preserving encryption, database search, oblivious random access memory, private information retrieval

Abstract. We propose a general construction of deterministic encryption schemes that unifies prior work and gives novel schemes. Specifically, its instantiations provide: -A construction from any trapdoor function that has sufficiently many hardcore bits. -A construction that provides "bounded" multi-message security from lossy trapdoor functions.The security proofs for these schemes are enabled by three tools that are of broader interest:-A weaker and more precise sufficient condition for semantic security on a high-entropy message distribution. Namely, we show that to establish semantic security on a distribution M of messages, it suffices to establish indistinguishability for all conditional distribution M |E, where E is an event of probability at least 1/4. (Prior work required indistinguishability on all distributions of a given entropy.) -A result about computational entropy of conditional distributions.Namely, we show that conditioning on an event E of probability p reduces the quality of computational entropy by a factor of p and its quantity by log 2 1/p. -A generalization of leftover hash lemma to correlated distributions.We also extend our result about computational entropy to the average case, which is useful in reasoning about leakage-resilient cryptography: leaking λ bits of information reduces the quality of computational entropy by a factor of 2 λ and its quantity by λ.

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Herder et al. (IEEE Transactions on Dependable and Secure Computing, 2017) designed a new computational fuzzy extractor and physical unclonable function (PUF) challenge-response protocol based on the Learning Parity with Noise (LPN) problem. The protocol requires no irreversible state updates on the PUFs for security, like burning irreversible fuses, and can correct for significant measurement noise when compared to PUFs using a conventional (information theoretical secure) fuzzy extractor. However, Herder et al. did not implement their protocol. In this paper, we give the first implementation of a challenge response protocol based on computational fuzzy extractors. Our main insight is that "confidence information" does not need to be kept private, if the noise vector is independent of the confidence information, e.g., the bits generated by ring oscillator pairs which are physically placed close to each other. This leads to a construction which is a simplified version of the design of Herder et al. (also building on a ring oscillator PUF). Our simplifications allow for a dramatic reduction in area by making a mild security assumption on ring oscillator physical obfuscated key output bits.

Fuzzy extractors (Dodis et al., Eurocrypt 2004) convert repeated noisy readings of a high-entropy secret into the same uniformly distributed key. A minimum condition for the security of the key is the hardness of guessing a value that is similar to the secret, because the fuzzy extractor converts such a guess to the key.We define fuzzy min-entropy to quantify this property of a noisy source of secrets. Fuzzy minentropy measures the success of the adversary when provided with only the functionality of the fuzzy extractor, that is, the ideal security possible from a noisy distribution. High fuzzy min-entropy is necessary for the existence of a fuzzy extractor.We ask: is high fuzzy min-entropy a sufficient condition for key extraction from noisy sources? If only computational security is required, recent progress on program obfuscation gives evidence that fuzzy min-entropy is indeed sufficient. In contrast, information-theoretic fuzzy extractors are not known for many practically relevant sources of high fuzzy min-entropy.In this paper, we show that fuzzy min-entropy is sufficient for information theoretically secure fuzzy extraction. For every source distribution W for which security is possible we give a secure fuzzy extractor.Our construction relies on the fuzzy extractor knowing the precise distribution of the source W . A more ambitious goal is to design a single extractor that works for all possible sources. Our second main result is that this more ambitious goal is impossible: we give a family of sources with high fuzzy min-entropy for which no single fuzzy extractor is secure. We show three flavors of this impossibility result: for standard fuzzy extractors, for fuzzy extractors that are allowed to sometimes be wrong, and for secure sketches, which are the main ingredient of most fuzzy extractor constructions.

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The extraction of consistent and identifiable features from an image of the human iris is known as iris recognition. Identifying which pixels belong to the iris, known as segmentation, is the first stage of iris recognition. Errors in segmentation propagate to later stages. Current segmentation approaches are tuned to specific environments. We propose using a convolution neural network for iris segmentation. Our algorithm is accurate when trained in a single environment and tested in multiple environments. Our network builds on the Mask R-CNN framework (He et al., ICCV 2017). Our approach segments faster than previous approaches including the Mask R-CNN network. Our network is accurate when trained on a single environment and tested with a different sensors (either visible light or near-infrared). Its accuracy degrades when trained with a visible light sensor and tested with a near-infrared sensor (and vice versa). A small amount of retraining of the visible light model (using a few samples from a near-infrared dataset) yields a tuned network accurate in both settings. For training and testing, this work uses the Casia v4 Interval, Notre Dame 0405, Ubiris v2, and IITD datasets.