Abstract. In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius’ goal along the lines of semantic security cannot be achieved. Contrary to intuition, a variant of the result threatens the privacy even of someone not in the database. This state of affairs suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one’s privacy incurred by participating in a database. The techniques developed in a sequence of papers [8, 13, 3], culminating in those described in [12], can achieve any desired level of privacy under this measure. In many cases, extremely accurate information about the database can be provided while simultaneously ensuring very high levels of privacy. 1

We propose a new general primitive called lossy trapdoor functions (lossy TDFs), and realize it under a variety of different number theoretic assumptions, including hardness of the decisional Diffie-Hellman (DDH) problem and the worst-case hardness of standard lattice problems. Using lossy TDFs, we develop a new approach for constructing many important cryptographic primitives, including standard trapdoor functions, CCA-secure cryptosystems, collisionresistant hash functions, and more. All of our constructions are simple, efficient, and black-box. Taken all together, these results resolve some long-standing open problems in cryptography. They give the first known (injective) trapdoor functions based on problems not directly related to integer factorization, and provide the first known CCA-secure cryptosystem based solely on worst-case lattice assumptions.

We show that a number of recent definitions and constructions of fuzzy extractors are not adequate for multiple uses of the same fuzzy secret—a major shortcoming in the case of biometric applications. We propose two particularly stringent security models that specifically address the case of fuzzy secret reuse, respectively from an outsider and an insider perspective, in what we call a chosen perturbation attack. We characterize the conditions that fuzzy extractors need to satisfy to be secure, and present generic constructions from ordinary building blocks. As an illustration, we demonstrate how to use a biometric secret in a remote error tolerant authentication protocol that does not require any storage on the client’s side. 1

We show two efficient techniques enabling the use of biometric data to achieve mutual authentication or authenticated key exchange over a completely insecure (i.e., adversarially controlled) channel. In addition to achieving stronger security guarantees than the work of Boyen, we improve upon his solution in a number of other respects: we tolerate a broader class of errors and, in one case, improve upon the parameters of his solution and give a proof of security in the standard model. 1 Using Biometric Data for Secure Authentication Biometric data, as a potential source of high-entropy, secret information, havebeen suggested as a way to enable strong, cryptographically-secure authentication of human users without requiring them to remember or store traditionalcryptographic keys. Before such data can be used in existing cryptographic protocols, however, two issues must be addressed: first, biometric data are not uni-formly distributed and hence do not offer provable security guarantees if used

Abstract. This paper considers two questions in cryptography. Cryptography Secure Against Memory Attacks. A particularly devastating side-channel attack against cryptosystems, termed the “memory attack”, was proposed recently. In this attack, a significant fraction of the bits of a secret key of a cryptographic algorithm can be measured by an adversary if the secret key is ever stored in a part of memory which can be accessed even after power has been turned off for a short amount of time. Such an attack has been shown to completely compromise the security of various cryptosystems in use, including the RSA cryptosystem and AES. We show that the public-key encryption scheme of Regev (STOC 2005), and the identity-based encryption scheme of Gentry, Peikert and Vaikuntanathan (STOC 2008) are remarkably robust against memory attacks where the adversary can measure a large fraction of the bits of the secret-key, or more generally, can compute an arbitrary function of the secret-key of bounded output length. This is done without increasing the size of the secret-key, and without introducing any

This paper explores what kinds of information two parties must communicate in order to correct errors which occur in a shared secret string W. Any bits they communicate must leak a significant amount of information about W — that is, from the adversary’s point of view, the entropy of W will drop significantly. Nevertheless, we construct schemes with which Alice and Bob can prevent an adversary from learning any useful information about W. Specifically, if the entropy of W is sufficiently high, then there is no function f(W) which the adversary can learn from the error-correction information with significant probability. This leads to several new results: (a) the design of noise-tolerant “perfectly oneway” hash functions in the sense of Canetti et al. [7], which in turn leads to obfuscation of proximity queries for high entropy secrets W; (b) private fuzzy extractors [11], which allow one to extract uniformly random bits from noisy and nonuniform data W, while also insuring that no sensitive information about W is leaked; and (c) noise tolerance and stateless key re-use in the Bounded Storage Model, resolving the main open problem of Ding [10]. The heart of our constructions is the design of strong randomness extractors with the property that the source W can be recovered from the extracted randomness and any string W ′ which is close to W.

Biometric recognition offers a reliable and natural solution to the problem of user authentication in identity management systems. With the widespread deployment of biometric systems in various applications, there are increasing concerns about the security and privacy of biometric technology. Public confidence and acceptance of the biometrics technology will depend on the ability of system designers to demonstrate that these systems are robust, have low error rates and are tamper proof. We present a high-level categorization of the various vulnerabilities of a biometric system and discuss countermeasures that have been proposed to address these vulnerabilities. In particular, we focus on biometric template security which is an important issue because unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. Due to intra-user variability in the acquired biometric traits, ensuring the security of the template while maintaining the recognition performance is a challenging task. We present an overview of various biometric template protection schemes and discuss their advantages and limitations in terms of security, revocability and impact on matching accuracy. A template protection scheme with provable security and acceptable recognition performance has thus far remained elusive. Development of such a scheme is crucial as biometric systems are beginning to proliferate into the core physical and information infrastructure of our society.

Abstract. There is growing interest in quantitative theories of information flow in a variety of contexts, such as secure information flow, anonymity protocols, and side-channel analysis. Such theories offer an attractive way to relax the standard noninterference properties, letting us tolerate “small ” leaks that are necessary in practice. The emerging consensus is that quantitative information flow should be founded on the concepts of Shannon entropy and mutual information.Butauseful theory of quantitative information flow must provide appropriate security guarantees: if the theory says that an attack leaks x bits of secret information, then x should be useful in calculating bounds on the resulting threat. In this paper, we focus on the threat that an attack will allow the secret to be guessed correctly in one try. With respect to this threat model, we argue that the consensus definitions actually fail to give good security guarantees—the problem is that a random variable can have arbitrarily large Shannon entropy even if it is highly vulnerable to being guessed. We then explore an alternative foundation based on a concept of vulnerability (closely related to Bayes risk) and which measures uncertainty using Rényi’s min-entropy, rather than Shannon entropy. 1

Most of the work in the analysis of cryptographic schemes is concentrated in abstract adversarial models that do not capture side-channel attacks. Such attacks exploit various forms of unintended information leakage, which is inherent to almost all physical implementations. Inspired by recent side-channel attacks, especially the “cold boot attacks ” of Halderman et al. (USENIX Security ’08), Akavia, Goldwasser and Vaikuntanathan (TCC ’09) formalized a realistic framework for modeling the security of encryption schemes against a wide class of sidechannel attacks in which adversarially chosen functions of the secret key are leaked. In the setting of public-key encryption, Akavia et al. showed that Regev’s lattice-based scheme (STOC ’05) is resilient to any leakage of

We propose the first practical and secure way to integrate the iris biometric into cryptographic applications. A repeatable binary string, which we call a biometric key, is generated reliably from genuine iris codes. A well-known difficulty has been how to cope with the 10 to 20 % of error bits within an iris code and derive an errorfree key. To solve this problem, we carefully studied the error patterns within iris codes, and devised a two-layer error correction technique that combines Hadamard and Reed-Solomon codes. The key is generated from a subject’s iris image with the aid of auxiliary error-correction data, which do not reveal the key, and can be saved in a tamper-resistant token such as a smart card. The reproduction of the key depends on two factors: the iris biometric and the token. The attacker has to procure both of them to compromise the key. We evaluated our technique using iris samples from 70 different eyes, with 10 samples from each eye. We found that an error-free key can be reproduced reliably from genuine iris codes with a 99.5% success rate. We can generate up to 140 bits of biometric key, more than enough for 128-bit AES. The extraction of a repeatable binary string from biometrics opens new possible applications, where a strong binding is required between a person and cryptographic operations. For example, it is possible to identify individuals without maintaining a central database of biometric templates, to which privacy objections might be raised.