biometrics

The best paper title I’ve ever had: Thirdeye

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So as I get more into biometrics I have to think of cool paper titles.  This is a cool work on iris recognition that uses the triplet loss to train the underlying neural network.  We managed to think of the name ThirdEye which is just about perfect.  Iris recognition is usually four steps:

  1. Segmentation: split pixels into iris/non-iris,
  2. Normalization: split the variable size image into a fixed dimension,
  3. Feature extraction: remove noise and further reduce dimension, and
  4. Comparison: compare the resulting template with a previous version.

Using the triplet loss with modern neural networks we challenge the belief that normalization is helpful (at least in all situations).  Source is here: https://github.com/sohaib50k/ThirdEye—Iris-recognition-using-triplets

 

We were inspired by a similar paper called “Influence of Segmentation on deep iris recognition performance” by Lozej, Stepec, Struc, and Peer that asked if segmentation was useful.

8 Submissions to publication!!! Code Offset in the Exponent

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This paper is finally published.  Its my new record for number of needed submissions.  The worst part about it is that it was Luke’s first paper and this unnecessarily stunted his growth.

 

I think its very cool but I say that about everything I end up writing up!

Abstract: Fuzzy extractors transform a noisy source e into a stable key which can be reproduced from a nearby value e’. They are a fundamental tool for key derivation from biometric sources. This work introduces code offset in the exponent and uses this construction to build the first reusable fuzzy extractor that simultaneously supports structured, low entropy distributions with correlated symbols and confidence information. These properties are specifically motivated by the most pertinent applications—key derivation from biometrics and physical unclonable functions—which typically demonstrate low entropy with additional statistical correlations and benefit from extractors that can leverage confidence information for efficiency. Code offset in the exponent is a group encoding of the code offset construction (Juels and Wattenberg, CCS 1999) that stores the value e in a one-time pad which is sampled as a codeword, Ax, of a linear error-correcting code: Ax+ e. Rather than encoding Ax+ e directly, code offset in the exponent calls for encoding by exponentiation of a generator in a cryptographically strong group. We demonstrate security of the construction in the generic group model, establishing security whenever the inner product between the error distribution and all vectors in the null space of the code is unpredictable. We show this condition includes distributions supported by multiple prior fuzzy extractors. Our analysis also shows a prior construction of pattern matching obfuscation (Bishop et al., Crypto 2018) is secure for more distributions than previously known.

Iris Segmentation using CNNs

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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.

Reusable Authentication from the Iris

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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).

Public Key Cryptography with Noisy Private Keys

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New Paper: Public Key Cryptography with Noisy Private Keys

Abstract: Passwords bootstrap symmetric and asymmetric cryptography, tying keys to an individual user. Biometrics are intended to strengthen this tie. Unfortunately, biometrics exhibit noise between repeated readings. Fuzzy extractors (Dodis et al., Eurocrypt 2004) derive stable symmetric keys from noisy sources.

We ask if it is also possible for noisy sources to directly replace private keys in asymmetric cryptosystems. We propose a new primitive called public-key cryptosystems with noisy keys. Such a cryptosystem functions when the private key varies according to some metric. An intuitive solution is to combine a fuzzy extractor with a public key cryptosystem. Unfortunately, fuzzy extractors need static helper information to account for noise. This helper information creates fundamental limitations on the resulting cryptosytems.

To overcome these limitations, we directly construct public-key encryption and digital signature algorithms with noisy keys. The core of our constructions is a computational version of the fuzzy vault (Juels and Sudan, Designs, Codes, and Cryptography 2006). Security of our schemes is based on graded encoding schemes (Garg et al., Eurocrypt 2013, Garg et al., TCC 2016). Importantly, our public-key encryption algorithm is based on a weaker model of grading encoding. If functional encryption or indistinguishable obfuscation exist in this weaker model, they also exist in the standard model.

In addition, we use the computational fuzzy vault to construct the first reusable fuzzy extractor (Boyen, CCS 2004) supporting a linear fraction of errors.

Joint work with Charles Herder, Marten van Dijk, and Srinivas Devadas

Pseudoentropic Isometries

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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.

Abstract:

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.

Iris Biometric Security Challenges and Possible Solutions

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Gene Itkis, Venkat Chandar, Benjamin Fuller, Joseph Campbell, Robert Cunningham. Iris Biometric Security Challenges and Possible Solutions: For your eyes only? Using the iris as a key. IEEE Signal Processing Magazine, 2015.

Abstract

Biometrics were originally developed for identification, such as for criminal investigations. More recently, biometrics have been also utilized for authentication. Most biometric authentication systems today match a user?s biometric reading against a stored reference template generated during enrollment. If the reading and the template are sufficiently close, the authentication is considered successful and the user is authorized to access protected resources. This binary matching approach has major inherent vulnerabilities.