Lowen and I just received word of acceptance of our recent work to ISIT. This papers asks whether you can build a universal fuzzy extractor for all high fuzzy min-entropy distributions. That is, can we have one construction that always just works. Unfortunately, the answer is negative. It is possible to artificially construct families of distributions that are impossible to simultaneously secure. This paper shares a lot of techniques with prior work of myself, Reyzin, and Smith. Excited to talk about these techniques more with the information theory community!
Sohaib (who’s awesome!) just gave his first presentation on performing iris segmentation using fully convolutional neural nets. The paper was published at AMV 2018 which is a workshop at ACCV.
Abstract: 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.
This is a paper I’m very excited about with Peter Fenteany, a great undergrad at UConn.
Abstract: An obfuscated program reveals nothing about its design other than its input/output behavior. A digital locker is an obfuscated program that outputs a stored cryptographic key if and only if a user enters a previously stored password. A digital locker is private if it provides an adversary with no information with high probability. An ideal digital locker would also prevent an adversary from mauling an obfuscation on one password and key into a new program that obfuscates a related password or key. Such a primitive is achievable in the random oracle model. Komargodski and Yogev (Eurocrypt, 2018) constructed a simpler primitive: a non-malleable point function which is a digital locker with no key.
This work describes the first non-malleable digital locker. This construction is built in two main steps:
- Constructing non-malleable digital lockers for short keys. We present one construction for a single bit key and a second for a logarithmic length keys. These constructions can be safely composed with the same input password. This composed construction is non-malleable with respect to the password. Security relies on variants of the strong and power DDH assumptions.
- An extension to polynomial length keys that additionally provides nonmalleability over the stored key. This extension combines the digital locker for short keys and non-malleable codes, and seed- dependent condensers. Our use of seed-dependent condensers require the password distribution to be efficient sampleable. The seed condenser must be public and random but programmability is not required.
Nonmalleability for the password is ensured for functions that can be represented as low degree polynomials. Key nonmalleability is ensured for the class of functions prevented by the non-malleable code.
Joint Work with Chenglu, Charles, Ling, Ha, Srini, and Marten.
Abstract: 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.
I’m super excited to put out my first paper written solely with UConn students. James and Sailesh have put a ton of work into this. We build a full key derivation system from the human iris by integrating image processing and the crypto described in our previous paper. I’m particularly excited because I started working on this problem in graduate school and it felt like we’d never get to an actual implementation.
Abstract: Mobile platforms use biometrics for authentication. Unfortunately, biometrics exhibit noise between repeated readings. Due to the noise, biometrics are stored in plaintext, so device compromise completely reveals the user’s biometric value.
To limit privacy violations, one can use fuzzy extractors to derive a stable cryptographic key from biometrics (Dodis et al., Eurocrypt 2004). Unfortunately, fuzzy extractors have not seen wide deployment due to insufficient security guarantees. Current fuzzy extractors provide no security for real biometric sources and no security if a user enrolls the same biometric with multiple devices or providers.
Previous work claims key derivation systems from the iris but only under weak adversary models. In particular, no known construction securely handles the case of multiple enrollments. Canetti et al. (Eurocrypt 2016) proposed a new fuzzy extractor called sample-then-lock.
We construct biometric key derivation for the iris starting from sample-then-lock. Achieving satisfactory parameters requires modifying and coupling of the image processing and the cryptography. Our construction is implemented in Python and being open-sourced. Our system has the following novel features:
— 45 bits of security. This bound is pessimistic, assuming the adversary can sample strings distributed according to the iris in constant time. Such an algorithm is not known.
— Secure enrollment with multiple services.
— Natural incorporation of a password, enabling multifactor authentication. The structure of the construction allows the overall security to be sum of the security of each factor (increasing security to 79 bits).
Our paper (with Jeremy Blackthorne, Ben Kaiser, and Bulent Yener) on how malware authenticates was published at Latincrypt 2017. Abstract below:
Abstract: Malware needs to execute on a target machine while simultaneously keeping its payload confidential from a malware analyst. Standard encryption can be used to ensure the confidentiality, but it does not address the problem of hiding the key. Any analyst can find the decryption key if it is stored in the malware or derived in plain view.
One approach is to derive the key from a part of the environment which changes when the analyst is present. Such malware derives a key from the environment and encrypts its true functionality under this key.
In this paper, we present a formal framework for environmental authentication. We formalize the interaction between malware and analyst in three settings: 1) blind: in which the analyst does not have access to the target environment, 2) basic: where the analyst can load a single analysis toolkit on an effected target, and 3) resettable: where the analyst can create multiple copies of an infected environment. We show necessary and sufficient conditions for malware security in the blind and basic games and show that even under mild conditions, the analyst can always win in the resettable scenario.
Talk for our Latincrypt paper, “Environmental Authentication in Malware” with Jeremy Blackthorne, Ben Kaiser, and Bulent Yener. Happy to hear feedback on our thoughts.
I was excited to join the paper Pseudoentropic Isometries: A New framework for fuzzy extractor reusability by Quentin Alamélou, Paul-Edmond Berthier, Chloe Cachet, Stéphane Cauchie, Benjamin Fuller, Philippe Gaborit, and Sailesh Simhadri. This paper describes how to use the random oracle to build a reusable fuzzy extractor that corrects a linear fraction of errors. Presented at AsiaCCS 2018. The abstract is below.
Fuzzy extractors (Dodis et al., Eurocrypt 2004) turn a noisy secret into a stable, uniformly distributed key. Reusable fuzzy extractors remain secure when multiple keys are produced from a single noisy secret (Boyen, CCS 2004). Boyen proved that any information-theoretically secure reusable fuzzy extractor is subject to strong limitations. Simoens et al. (IEEE S&P, 2009) then showed deployed constructions suffer severe security breaks when reused. Canetti et al. (Eurocrypt 2016) proposed using computational security to sidestep this problem. They constructed a computationally secure reusable fuzzy extractor for the Hamming metric that corrects a sublinear fraction of errors.
We introduce a generic approach to constructing reusable fuzzy extractors. We define a new primitive called a reusable pseudoentropic isometry that projects an input metric space to an output metric space. This projection preserves distance and entropy even if the same input is mapped to multiple output metric spaces. A reusable pseudoentropy isometry yields a reusable fuzzy extractor by 1) randomizing the noisy secret using the isometry and 2) applying a traditional fuzzy extractor to derive a secret key.
We propose reusable pseudoentropic isometries for the set difference and Hamming metrics. The set difference construction is built from composable digital lockers (Canetti and Dakdouk, Eurocrypt 2008) yielding the first reusable fuzzy extractor that corrects a linear fraction of errors. For the Hamming metric, we show that the second construction of Canetti et al. (Eurocrypt 2016) can be seen as an instantiation of our framework. In both cases, the pseudoentropic isometry’s reusability requires noisy secrets distributions to have entropy in each symbol of the alphabet.
Lastly, we implement our set difference solution and describe two use cases.
On September 7, I presented a high level overview about passwords to the UConn Cyber Security club. This is a fun introduction to passwords and what people are trying to do to fix them. Slides are included