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  1. Memory Hard Functions (MHFs) have been proposed as an answer to the growing inequality between the computational speed of general purpose CPUs and ASICs. MHFs have seen widespread applications including password hashing, key stretching and proofs of work. Several metrics have been proposed to quantify the memory hardness of a function. Cumulative memory complexity (CMC) quantifies the cost to acquire/build the hardware to evaluate the function repeatedly at a given rate. By contrast, bandwidth hardness quantifies the energy costs of evaluating this function. Ideally, a good MHF would be both bandwidth hard and have high CMC. While the CMC of leading MHF candidates is well understood, little is known about the bandwidth hardness of many prominent MHF candidates. Our contributions are as follows: First, we provide the first reduction proving that, in the parallel random oracle model (pROM), the bandwidth hardness of a data-independent MHF (iMHF) is described by the red-blue pebbling cost of the directed acyclic graph associated with that iMHF. Second, we show that the goals of designing an MHF with high CMC/bandwidth hardness are well aligned. Any function (data-independent or not) with high CMC also has relatively high bandwidth costs. Third, we prove that in the pROM the prominent iMHF candidates such as Argon2i, aATSample and DRSample are maximally bandwidth hard. Fourth, we prove the first unconditional tight lower bound on the bandwidth hardness of a prominent data-dependent MHF called Scrypt in the pROM. Finally, we show the problem of finding the minimum cost red–blue pebbling of a directed acyclic graph is NP-hard. 
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    Free, publicly-accessible full text available April 1, 2025
  2. Galdi, Celemente ; Jarecki, Stanislaw (Ed.)
    In the past decade billions of user passwords have been exposed to the dangerous threat of offline password cracking attacks. An offline attacker who has stolen the cryptographic hash of a user's password can check as many password guesses as s/he likes limited only by the resources that s/he is willing to invest to crack the password. Pepper and key-stretching are two techniques that have been proposed to deter an offline attacker by increasing guessing costs. Pepper ensures that the cost of rejecting an incorrect password guess is higher than the (expected) cost of verifying a correct password guess. This is useful because most of the offline attacker's guesses will be incorrect. Unfortunately, as we observe the traditional peppering defense seems to be incompatible with modern memory hard key-stretching algorithms such as Argon2 or Scrypt. We introduce an alternative to pepper which we call Cost-Asymmetric Memory Hard Password Authentication which benefits from the same cost-asymmetry as the classical peppering defense i.e., the cost of rejecting an incorrect password guess is larger than the expected cost to authenticate a correct password guess. When configured properly we prove that our mechanism can only reduce the percentage of user passwords that are cracked by a rational offline attacker whose goal is to maximize (expected) profit i.e., the total value of cracked passwords minus the total guessing costs. We evaluate the effectiveness of our mechanism on empirical password datasets against a rational offline attacker. Our empirical analysis shows that our mechanism can reduce the percentage of user passwords that are cracked by a rational attacker by up to 10%. 
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    Free, publicly-accessible full text available March 1, 2025
  3. Free, publicly-accessible full text available September 9, 2024
  4. We revisit computationally relaxed locally decodable codes (crLDCs) (Blocki et al., Trans. Inf. Theory ’21) and give two new constructions. Our first construction is a Hamming crLDC that is conceptually simpler than prior constructions, leveraging digital signature schemes and an appropriately chosen Hamming code. Our second construction is an extension of our Hamming crLDC to handle insertion-deletion (InsDel) errors, yielding an InsDel crLDC. This extension crucially relies on the noisy binary search techniques of Block et al. (FSTTCS ’20) to handle InsDel errors. Both crLDC constructions have binary codeword alphabets, are resilient to a constant fraction of Hamming and InsDel errors, respectively, and under suitable parameter choices have poly-logarithmic locality and encoding length linear in the message length and polynomial in the security parameter. These parameters compare favorably to prior constructions in the poly-logarithmic locality regime. 
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  5. Proper communication is key to the adoption and implementation of differential privacy (DP). In this work, we designed explanative illustrations of three DP models (Central DP, Local DP, Shuffler DP) to help laypeople conceptualize how random noise is added to protect individuals’ privacy and preserve group utility. Following a pilot survey and an interview, we conducted an online experiment ( N = 300) exploring participants’ comprehension, privacy and utility perception, and data-sharing decisions across the three DP models. We obtained empirical evidence showing participants’ acceptance of the Shuffler DP model for data privacy protection. We discuss the implications of our findings.

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    Free, publicly-accessible full text available September 1, 2024
  6. In password security a defender would like to identify and warn users with weak passwords. Similarly, the defender may also want to predict what fraction of passwords would be cracked within B guesses as the attacker’s guessing budget B varies from small (online attacker) to large (offline attacker). Towards each of these goals the defender would like to quickly estimate the guessing number for each user password pwd assuming that the attacker uses a password cracking model M i.e., how many password guesses will the attacker check before s/he cracks each user password pwd. Since naïve brute-force enumeration can be prohibitively expensive when the guessing number is very large, Dell’Amico and Filippone [1] developed an efficient Monte Carlo algorithm to estimate the guessing number of a given password pwd. While Dell’Amico and Filippone proved that their estimator is unbiased there is no guarantee that the Monte Carlo estimates are accurate nor does the method provide confidence ranges on the estimated guessing number or even indicate if/when there is a higher degree of uncertainty.Our contributions are as follows: First, we identify theoretical examples where, with high probability, Monte Carlo Strength estimation produces highly inaccurate estimates of individual guessing numbers as well as the entire guessing curve. Second, we introduce Confident Monte Carlo Strength Estimation as an extension of Dell’Amico and Filippone [1]. Given a password our estimator generates an upper and lower bound with the guarantee that, except with probability δ, the true guessing number lies within the given confidence range. Our techniques can also be used to characterize the attacker’s guessing curve. In particular, given a probabilistic password cracking model M we can generate high confidence upper and lower bounds on the fraction of passwords that the attacker will crack as the guessing budget B varies. 
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  7. A central challenge in password security is to characterize the attacker's guessing curve i.e., what is the probability that the attacker will crack a random user's password within the first G guesses. A key challenge is that the guessing curve depends on the attacker's guessing strategy and the distribution of user passwords both of which are unknown to us. In this work we aim to follow Kerckhoffs's principal and analyze the performance of an optimal attacker who knows the password distribution. Let \lambda_G denote the probability that such an attacker can crack a random user's password within G guesses. We develop several statistically rigorous techniques to upper and lower bound \lambda_G given N independent samples from the unknown password distribution P. We show that our upper/lower bounds on \lambda_G hold with high confidence and we apply our techniques to analyze eight large password datasets. Our empirical analysis shows that even state-of-the-art password cracking models are often significantly less guess efficient than an attacker who can optimize its attack based on its (partial) knowledge of the password distribution. We also apply our statistical tools to re-examine different models of the password distribution i.e., the empirical password distribution and Zipf's Law. We find that the empirical distribution closely matches our upper/lower bounds on \lambda_G when the guessing number G is not too large i.e., G << N. However, for larger values of G our empirical analysis rigorously demonstrates that the empirical distribution (resp. Zipf's Law) overestimates the attacker's success rate. We apply our statistical techniques to upper/lower bound the effectiveness of password throttling mechanisms (key-stretching) which are used to reduce the number of attacker guesses G. Finally, if we are willing to make an additional assumption about the way users respond to password restrictions, we can use our statistical techniques to evaluate the effectiveness of various password composition policies which restrict the passwords that users may select. 
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  8. The data management of large companies often prioritize more recent data, as a source of higher accuracy prediction than outdated data. For example, the Facebook data policy retains user search histories for months while the Google data retention policy states that browser information may be stored for up to months. These policies are captured by the sliding window model, in which only the most recent statistics form the underlying dataset. In this paper, we consider the problem of privately releasing the L2-heavy hitters in the sliding window model, which include Lp-heavy hitters for p<=2 and in some sense are the strongest possible guarantees that can be achieved using polylogarithmic space, but cannot be handled by existing techniques due to the sub-additivity of the L2 norm. Moreover, existing non-private sliding window algorithms use the smooth histogram framework, which has high sensitivity. To overcome these barriers, we introduce the first differentially private algorithm for L2-heavy hitters in the sliding window model by initiating a number of L2-heavy hitter algorithms across the stream with significantly lower threshold. Similarly, we augment the algorithms with an approximate frequency tracking algorithm with significantly higher accuracy. We then use smooth sensitivity and statistical distance arguments to show that we can add noise proportional to an estimation of the norm. To the best of our knowledge, our techniques are the first to privately release statistics that are related to a sub-additive function in the sliding window model, and may be of independent interest to future differentially private algorithmic design in the sliding window model. 
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  9. Kiltz, E. (Ed.)
    The classical (parallel) black pebbling game is a useful abstraction which allows us to analyze the resources (space, space-time, cumulative space) necessary to evaluate a function f with a static data-dependency graph G. Of particular interest in the field of cryptography are data-independent memory-hard functions fG,H which are defined by a directed acyclic graph (DAG) G and a cryptographic hash function H. The pebbling complexity of the graph G characterizes the amortized cost of evaluating fG,H multiple times as well as the total cost to run a brute-force preimage attack over a fixed domain X, i.e., given y∈{0,1}∗ find x∈X such that fG,H(x)=y. While a classical attacker will need to evaluate the function fG,H at least m=|X| times a quantum attacker running Grover’s algorithm only requires O(m−−√) blackbox calls to a quantum circuit CG,H evaluating the function fG,H. Thus, to analyze the cost of a quantum attack it is crucial to understand the space-time cost (equivalently width times depth) of the quantum circuit CG,H. We first observe that a legal black pebbling strategy for the graph G does not necessarily imply the existence of a quantum circuit with comparable complexity—in contrast to the classical setting where any efficient pebbling strategy for G corresponds to an algorithm with comparable complexity for evaluating fG,H. Motivated by this observation we introduce a new parallel reversible pebbling game which captures additional restrictions imposed by the No-Deletion Theorem in Quantum Computing. We apply our new reversible pebbling game to analyze the reversible space-time complexity of several important graphs: Line Graphs, Argon2i-A, Argon2i-B, and DRSample. Specifically, (1) we show that a line graph of size N has reversible space-time complexity at most O(N^{1+2/√logN}). (2) We show that any (e, d)-reducible DAG has reversible space-time complexity at most O(Ne+dN2^d). In particular, this implies that the reversible space-time complexity of Argon2i-A and Argon2i-B are at most O(N^2 loglogN/√logN) and O(N^2/(log N)^{1/3}), respectively. (3) We show that the reversible space-time complexity of DRSample is at most O((N^2loglog N)/log N). We also study the cumulative pebbling cost of reversible pebblings extending a (non-reversible) pebbling attack of Alwen and Blocki on depth-reducible graphs. 
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