Extraction of uniform randomness from (noisy) non-uniform sources is an important primitive in many security applications, e.g. (pseudo-)random number generators, privacy-preserving biometrics, and key storage based on Physical Unclonable Functions. Generic extraction methods exist, using universal hash functions. There is a trade-off between the length of the extracted bit string and the uniformity of the string. In the literature there are proven lower bounds on this length as a function of the desired uniformity. The best known bound involves a quantity known as smooth min-entropy. Unfortunately, there exist at least three definitions of smooth entropy. In this paper we compare three of these definitions, and we derive improved lower bounds on the extractable randomness.\nWe also investigate the use of almost universal hash functions, which are slightly worse at extracting randomness than universal hash functions, but are preferable in practice because they require far less resources in devices. We show that using them has negligible effect on the extractable randomness.\nKeywords: Leftover Hash Lemma; Universal hash function; Randomness extraction
Extraction of uniform randomness from (noisy) non-uniform sources is an important primitive in many security applications, e.g. (pseudo-)random number generators, privacy-preserving biometrics, and key storage based on Physical Unclonable Functions. Generic extraction methods exist, using universal hash functions. There is a trade-off between the length of the extracted bit string and the uniformity of the string. In the literature there are proven lower bounds on this length as a function of the desired uniformity. The best known bound involves a quantity known as smooth min-entropy. Unfortunately, there exist at least three definitions of smooth entropy. In this paper we compare three of these definitions, and we derive improved lower bounds on the extractable randomness.\nWe also investigate the use of almost universal hash functions, which are slightly worse at extracting randomness than universal hash functions, but are preferable in practice because they require far less resources in devices. We show that using them has negligible effect on the extractable randomness.\nKeywords: Leftover Hash Lemma; Universal hash function; Randomness extraction
A fuzzy extractor is a security primitive that allows for reproducible extraction of an almost uniform key from a non-uniform noisy source. We analyze a fuzzy extractor scheme that uses universal hash functions for both information reconciliation and privacy amplification. This is a useful scheme when the number of error patterns likely to occur is limited, regardless of the error probabilities. We derive a sharp bound on the uniformity of the extracted key, making use of the concatenation property of universal hash functions and a recent tight formulation of the leftover hash lemma.
We introduce smooth entropy as a measure for the number of almost uniform random bits that can be extracted from a source by probabilistic algorithms. The extraction process should be universal in the sense that it does not require the distribution of the source to be known. Rather, it should work for all sources with some structural property. Smooth entropy unifies previous work on privacy amplification and entropy smoothing. It enables a systematic investigation of the spoiling knowledge proof technique to obtain lower bounds on smooth entropy and to show new connections to Renyi entropy of order /spl alpha/>1.
Most cryptographic primitives require randomness (for example, to generate secret keys). Usually, one assumes that perfect randomness is available, but, conceivably, such primitives might be built under weaker, more realistic assumptions. This is known to be achievable for many authentication applications, when entropy alone is typically sufficient. In contrast, all known techniques for achieving privacy seem to fundamentally require (nearly) perfect randomness. We ask the question whether this is just a coincidence, or, perhaps, privacy inherently requires true randomness? We completely resolve this question for information-theoretic private-key encryption, where parties wish to encrypt a b-bit value using a shared secret key sampled from some imperfect source of randomness [special characters omitted]. Our main result shows that if such n-bit source [special characters omitted] allows for a secure encryption of b bits, where b > log n, then one can deterministically extract nearly b almost perfect random bits from [special characters omitted]. Further, the restriction that b > log n is nearly tight: there exist sources [special characters omitted] allowing one to perfectly encrypt (log n – loglog n) bits, but not to deterministically extract even a single slightly unbiased bit. Hence, to a large extent, true randomness is inherent for encryption : either the key length must be exponential in the message length b, or one can deterministically extract nearly b almost unbiased random bits from the key. In particular, the one-time pad scheme is essentially "universal". Our technique also extends to related primitives which are sufficiently binding and hiding, including computationally secure commitments and public-key encryption.
Physical and quantum random number generators (QRNGs) do not emit uniform bits: detector bias, dead time, after-pulsing, and environmental drift leave their raw output biased, correlated, and partially predictable to an adversary who models the device. Deployable generators therefore include a conditioning (randomness-extraction) stage that distils near-uniform keys from a source of certified min-entropy, and the cryptographic workhorse of that stage is the Leftover Hash Lemma (LHL). This paper analyses two ways in which real deployments depart from the textbook LHL. First, a physical source has no fixed min-entropy: continuous health tests, as standardised in NIST SP 800-90B, produce a runtime lower-bound estimate that varies from block to block and can occasionally be optimistic. Driving the extractor output length from this estimate, we prove an online adaptive extraction theorem whose security bound cleanly composes the estimator soundness error with the extractor error, yielding a (δ+ε)-secure key and separating how often the entropy estimate is optimistic from how uniform the hash output is, as two independently tunable budgets. Second, for settings that publish or reuse the extractor seed — public randomness beacons, reproducible pipelines, and seed-constrained devices — we prove that publishing the seed costs nothing, since this is the defining property of a strong extractor, and that reusing a single seed across m independent source blocks costs at most mε in statistical distance. A corollary bounds the security loss when the extracted key is consumed by a downstream symmetric primitive, and a worked instantiation with realistic QRNG parameters reports the achievable secret-key rate. A reproducible numerical simulation on a synthetic biased source corroborates all four claims. Throughout, we are explicit about the single load-bearing unproven premise — empirical min-entropy certification of the physical device — which belongs to entropy-source validation and lies outside the scope of the analysis.
Randomness plays a central role in computer science, in particular cryptography. Almost all cryptographic primitives depend crucially on randomness because randomness and unpredictability in secret keys provides the means for security. Usually one assumes that perfect randomness, a sequence of independently and uniformly distributed bits, are accessible to algorithms. This is a strong assumption. Physical sources of randomness are neither uniformly random, nor produce necessarily independent bits. Therefore, the aim of this thesis is to start from a realistic model of randomness, investigate notions of secrecy and their randomness requirements and finally find practical methods for generation of randomness that matches the requirements of cryptographic primitives. We consider a model of random source where the source output follows one distribution from a set of possible distributions, each with the property that the maximum probability of symbols is bounded and can not be arbitrarily close to 1. This model does not assume independence or uniformity of the output symbols, and is considered to be a realistic model of randomness. From this point, the thesis can be divided into two main parts: In the first part, considering various notions of information theoretic secrecy, a fundamental problem is to find the properties of randomness needed to achieve security in these notions. Traditional cryptographic protocols simply assume perfect randomness and build on this assumption. We explore the results that show secrecy can not be based on imperfect randomness that is not uniform or independent. Thus a line of work attempts to relax notions of secrecy in such a way that they can be constructed with non-perfect sources, and possibly require smaller key sizes. Yet they should match real life applications. An important work in this context is entropic security where the key size could be smaller than the message depending on message distribution. Inspired by this, we propose two relaxed notions of secrecy that are motivated by practical applications. In the first notion, motivated by an application in biometrics authentication, we propose guessing secrecy where the probability of guessing the message for a computational unbounded adversary with the best strategy remains the same when a ciphertext is given or when it is not. We compare the randomness requirements of guessing secrecy with stronger notions and show that in some cases such as key length, the requirements are the same. For key distribution however, we found a family of distributions that provide guessing secrecy but not perfect secrecy. In the second notion, we investigate randomness requirements of multiple message encryption. Considering a natural extension of secrecy definition to multiple messages, we show that independent keys are needed to encrypt each message. We then propose a relaxed notion in which the security of last message is more important than past messages, although the leakage of past messages is bounded using entropic security. By this assumption, we achieve smaller key length compared to indistinguishability, and comparable to key length for entropic security. This notion has applications such as location privacy. In the second part of the thesis, since secrecy crucially depends on perfect randomness, we investigate how perfect randomness can be practically generated, specifically from human game-play. Unlike many random number generators that assume independent or uniform random bits in the random source, we base our work on the realistic model of randomness. Our main observation is that human game-play has an element of randomness coming from the errors in their game-play which is the main entertaining factor of the game. We also observed that this game-play can distinguish among a group of people if the right features are collected from the game-play. We incrementally changed our game design until the distinguishability among a small population is maximized, and then run the experiments required to show the viability of this approach over a larger population. This approach can also provide a hard to delegate authentication property where a human could not emulate the behavior of another human even given statistical information about their game-play.
One of the most important properties of a cryptographic system is a proof of its security. In the present work, information-theoretic methods are used for proving the security of unconditionally secure cryptosystems. The security of such systems does not depend on unproven intractability assumptions. A survey of entropy measures and their applications in cryptography is presented. A new information measure, smooth entropy, is introduced to quantify the number of almost uniform random bits that can be extracted from a source by probabilistic algorithms. Smooth entropy unifies previous work on privacy amplification in cryptography and on entropy smoothing in theoretical computer science. It enables a systematic investigation of the spoiling knowledge proof technique to obtain lower bounds on smooth entropy. The R'enyi entropy of order at least 2 of a random variable is a lower bound for its smooth entropy, whereas an assumption about R'enyi entropy of order 1, which is equivalent to the ...