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).
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
Robert Cunningham, Benjamin Fuller, and Sophia Yakoubov. Catching MPC Cheaters: Identification and Openability. 2016.
Secure multi-party computation (MPC) protocols do not completely prevent malicious parties from cheating and disrupting the computation. A coalition of malicious parties can repeatedly cause the computation to abort or provide an input that does not correspond to reality. In this work, we augment MPC with two new properties to discourage cheating. The first of these is a strengthening of identifiable abort where all parties who do not follow the protocol will be identified as cheaters by each honest party. The second is openability, which means that if a computation output is discovered to be untrue (e.g. by a real-world event contradicting it), a distinguished coalition of parties can recover the MPC inputs. We provide the first efficient MPC protocol achieving both of those properties. Our scheme extends the SPDZ protocol (Damgard et al., Crypto 2012). SPDZ leverages an offline (computation- independent) pre-processing phase to speed up the online computation. Our protocol is optimistic: it has the same communication and computation complexity in the online phase as SPDZ when no parties cheat. If cheating does occur, each honest party can additionally perform a local computation to identify all cheaters. We achieve identifiable abort by using a new locally identifiable secret sharing scheme (as defined by Ishai, Ostrovsky, and Zikas (TCC 2012)) which we call commitment enhanced secret sharing, or CESS. In CESS, each SPDZ input share is augmented with a linearly homomorphic commitment. When cheating occurs, each party can use the linear homomorphism to compute a commitment to the corresponding share of the output value. Parties whose claimed output share does not match their output share commitments are identified as cheaters. We achieve openability through the use of verifiable encryption and specialized zero-knowledge proofs. Openability relies on the availability of an auditable public transcript of the MPC execution, as introduced by Baum, Damgard, and Orlandi (SCN 2014).