arXiv subjects

Dean Doron

Publications and source records attributed to Dean Doron.

10 recordsLinked to original sources

arXiv2026

A Forward-Backward Weight Analysis of INW for Permutation Branching Programs

We construct an $\varepsilon$-error PRG for permutation read-once branching programs of length $n$ and width $w$ with seed length \[ O\left((\log w+\log(1/\varepsilon))\cdot \log n\right). \] This gives an exponential improvement in the dependence on $w$ compared with the constructions of De (CCC 2011) and Steinke (ECCC 2012). Compared with the work of Braverman, Rao, Raz, and Yehudayoff (FOCS 2010; SICOMP 2014), which applies more generally to regular branching programs and already achieves the optimal dependence on $w$, our result improves the dependence on the length $n$, attaining the optimal logarithmic dependence. The generator itself is the classical INW PRG of Impagliazzo, Nisan, and Wigderson (STOC 1994). We show that, for permutation branching programs, the INW generator can be instantiated with expanders whose degrees are polynomial in $w$ and $1/\varepsilon$ and, crucially, independent of $n$. To prove this, we analyze error propagation using program-dependent seminorms tailored to the branching program at hand. These seminorms build on the weight function introduced by Braverman et al. The key point is that, when measured in these adapted seminorms, the error does not accumulate throughout the recursion. Since our analysis relies only on the spectral expansion of the underlying expanders, our seed length tightly matches the recent lower bound for spectral analyses of the INW generator due to Hoza, Pyne, and Vadhan (Algorithmica 2024).

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arXiv2026

The Insertion List-Decoding Capacity and an Improved Bound on the Deletion List-Decoding Capacity

Informally, the capacity of list-decoding in a given adversarial error model is the largest rate at which we can list-decode with list size polynomial in the block length. The capacity of list-decoding from insertions and deletions is a basic, yet poorly understood, aspect of coding against synchronization errors. For example, when dealing with a $\delta>1/2$ fraction of insertions, the best known lower bounds give little more than the fact that the capacity is positive. Beyond that regime we also only have loose bounds, with the lower bounds stemming from the analysis of uniformly random codes. We make progress in our understanding of the limits of list-decoding binary codes from insertions and deletions. We show that the capacity of list-decoding from a $\delta$-fraction of insertions is exactly \begin{equation*} (1+\delta)\left(1-h\left(\frac{\delta}{1+\delta}\right)\right) \end{equation*} for all $\delta\in[0,1]$, achieved with high probability by a code sampled according to a symmetric $2$-state Markov chain. Curiously, we complement this by showing that such an approach does not beat uniformly random coding in list-decoding from deletions. We also give an improved upper bound on the capacity of list-decoding from a $\delta$-fraction of deletions, showing in particular that it behaves like $1-h(\delta)$ when $\delta\to 0$. This matches the asymptotic behavior of the capacity of the binary deletion channel for vanishing deletion probability.

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arXiv2026

Discrepancy for Random Linear Codes

We prove that random linear codes have nearly optimal discrepancy properties in a broad range of regimes. Our main results are two general theorems: one controlling all translates of a fixed test, and another controlling large families of Fourier-pseudorandom tests. Two motivating applications follow. First, random linear codes match unstructured random codes for list-decoding from errors above capacity. If $C\subseteq\mathbb F_q^n$ is a random linear code of rate $1-\frac1n\log_q |B_\rho|+\epsilon$, where $B_\rho$ is a radius-$\rho$ Hamming ball, then with high probability $$ |C\cap B|=(1\pm o(1))\frac{|C||B|}{q^n} $$ simultaneously for all radius-$\rho$ Hamming balls $B\subseteq\mathbb F_q^n$. This extends the classical result that such codes have covering radius at most $\rho n$ whp (Blinovsky, 1987). Second, over prime fields, random linear codes match unstructured random codes for zero-error list-recovery above capacity. For prime $q>2$ and $2\le \ell\le q-1$, a random linear code of rate $1-\log_q\ell+\epsilon$ satisfies, with high probability, $$ |C\cap S|=(1\pm o(1))\frac{|C|\ell^n}{q^n} $$ simultaneously for all rectangles $S=S_1\times\cdots\times S_n$ with $|S_i|=\ell$. As a consequence, there are abundant $n$-party linear ramp secret sharing schemes over $\mathbb F_q$ with privacy threshold about $n/(2\log q)$ and reconstruction threshold about $5n/(2\log q)$, resilient to balanced local leakage; prior existence results required thresholds above $n/2$ even in this case. The translate result, hence the list-decoding application, holds over arbitrary finite fields, even growing with $n$. The list-recovery and leakage applications hold over prime fields under moderate growth, e.g. $q\le n^{1/5-o(1)}$. The proofs use a refined second-moment analysis tracking intersection sizes as random generators are added to $C$.

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arXiv2026

Optimal PRGs for Low-Degree Polynomials over Polynomial-Size Fields

Pseudorandom generators (PRGs) for low-degree polynomials are a central object in pseudorandomness, with applications to circuit lower bounds and derandomization. Viola's celebrated construction gives a PRG over the binary field, but with seed length exponential in the degree $d$. This exponential dependence can be avoided over sufficiently large fields. In particular, Dwivedi, Guo, and Volk constructed PRGs with optimal seed length over fields of size exponential in $d$. The latter builds on the framework of Derksen and Viola, who obtained optimal-seed constructions over fields of size polynomial in $d$, although growing with the number of variables $n$. In this work, we construct the first PRG with optimal seed length for degree-$d$ polynomials over fields of polynomial size, specifically $q \approx d^4$, assuming sufficiently large characteristic. Our construction follows the framework of prior work and reduces the required field size by replacing the hitting-set generator used in previous constructions with a new pseudorandom object. We also observe a threshold phenomenon in the field-size dependence. Specifically, we prove that constructing PRGs over fields of sublinear size, for example $q = d^{0.99}$ where $q$ is a power of two, would already yield PRGs for the binary field with comparable seed length via our reduction, provided that the construction imposes no restriction on the characteristic. While a breakdown of existing techniques has been noted before, we prove that this phenomenon is inherent to the problem itself, irrespective of the technique used.

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arXiv2025

Tracing AG Codes: Toward Meeting the Gilbert-Varshamov Bound

One of the oldest problems in coding theory is to match the Gilbert-Varshamov bound with explicit binary codes. Over larger-yet still constant-sized-fields, algebraic-geometry codes are known to beat the GV bound. In this work, we leverage this phenomenon by taking traces of AG codes. Our hope is that the margin by which AG codes exceed the GV bound will withstand the parameter loss incurred by taking the trace from a constant field extension to the binary field. In contrast to concatenation, the usual alphabet-reduction method, our analysis of trace-of-AG (TAG) codes uses the AG codes' algebraic structure throughout - including in the alphabet-reduction step. Our main technical contribution is a Hasse-Weil-type theorem that is well-suited for the analysis of TAG codes. The classical theorem (and its Grothendieck trace-formula extension) are inadequate in this setting. Although we do not obtain improved constructions, we show that a constant-factor strengthening of our bound would suffice. We also analyze the limitations of TAG codes under our bound and prove that, in the high-distance regime, they are inferior to code concatenation. Our Hasse-Weil-type theorem holds in far greater generality than is needed for analyzing TAG codes. In particular, we derive new estimates for exponential sums.

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arXiv2025

List-Recovery of Random Linear Codes over Small Fields

We study list-recoverability of random linear codes over small fields, both from errors and from erasures. We consider codes of rate $\epsilon$-close to capacity, and aim to bound the dependence of the output list size $L$ on $\epsilon$, the input list size $\ell$, and the alphabet size $q$. Prior to our work, the best upper bound was $L = q^{O(\ell/\epsilon)}$ (Zyablov and Pinsker, Prob. Per. Inf. 1981). Previous work has identified cases in which linear codes provably perform worse than non-linear codes with respect to list-recovery. While there exist non-linear codes that achieve $L=O(\ell/\epsilon)$, we know that $L \ge \ell^{\Omega(1/\epsilon)}$ is necessary for list recovery from erasures over fields of small characteristic, and for list recovery from errors over large alphabets. We show that in other relevant regimes there is no significant price to pay for linearity, in the sense that we get the correct dependence on the gap-to-capacity $\epsilon$ and go beyond the Zyablov-Pinsker bound for the first time. Specifically, when $q$ is constant and $\epsilon$ approaches zero: - For list-recovery from erasures over prime fields, we show that $L \leq C_1/\epsilon$. By prior work, such a result cannot be obtained for low-characteristic fields. - For list-recovery from errors over arbitrary fields, we prove that $L \leq C_2/\epsilon$. Above, $C_1$ and $C_2$ depend on the decoding radius, input list size, and field size. We provide concrete bounds on the constants above, and the upper bounds on $L$ improve upon the Zyablov-Pinsker bound whenever $q\leq 2^{(1/\epsilon)^c}$ for some small universal constant $c>0$.

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arXiv2024

Nearly-Linear Time Seeded Extractors with Short Seeds

Seeded extractors are fundamental objects in pseudorandomness and cryptography, and a deep line of work has designed polynomial-time seeded extractors with nearly-optimal parameters. However, existing constructions of seeded extractors with short seed length and large output length run in time $\Omega(n \log(1/\varepsilon))$ and often slower, where $n$ is the input source length and $\varepsilon$ is the error of the extractor. Since cryptographic applications of extractors require $\varepsilon$ to be small, the resulting runtime makes these extractors impractical. Motivated by this, we explore constructions of strong seeded extractors with short seeds computable in nearly-linear time $O(n \log^c n)$, for any error $\varepsilon$. We show that an appropriate combination of modern condensers and classical approaches for constructing seeded extractors for high min-entropy sources yields such extractors. More precisely, we obtain strong extractors for $n$-bit sources with any min-entropy $k$ and any target error $\varepsilon$ with seed length $d=O(\log(n/\varepsilon))$ and output length $m=(1-\eta)k$ for an arbitrarily small constant $\eta>0$, running in nearly-linear time. When $k$ or $\varepsilon$ are very small, our construction requires a reasonable one-time preprocessing step. These extractors directly yield privacy amplification protocols with nearly-linear time complexity (possibly after a one-time preprocessing step), large output length, and low communication complexity. As a second contribution, we give an instantiation of Trevisan's extractor that can be evaluated in truly linear time in the RAM model, as long as the number of output bits is at most $\frac{n}{\log(1/\varepsilon)polylog(n)}$. Previous fast implementations of Trevisan's extractor ran in $\widetilde{O}(n)$ time in this setting.

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arXiv2024

When Do Low-Rate Concatenated Codes Approach The Gilbert-Varshamov Bound?

The Gilbert--Varshamov (GV) bound is a classical existential result in coding theory. It implies that a random linear binary code of rate $\epsilon^2$ has relative distance at least $\frac{1}{2} - O(\epsilon)$ with high probability. However, it is a major challenge to construct explicit codes with similar parameters. One hope to derandomize the Gilbert--Varshamov construction is with code concatenation: We begin with a (hopefully explicit) outer code ${C}_\mathrm{out}$ over a large alphabet, and concatenate that with a small binary random linear code ${C}_\mathrm{in}$. It is known that when we use independent small codes for each coordinate, then the result lies on the GV bound with high probability, but this still uses a lot of randomness. In this paper, we consider the question of whether code concatenation with a single random linear inner code ${C}_\mathrm{in}$ can lie on the GV bound; and if so what conditions on ${C}_\mathrm{out}$ are sufficient for this. We show that first, there do exist linear outer codes ${C}_\mathrm{out}$ that are "good" for concatenation in this sense (in fact, most linear codes codes are good). We also provide two sufficient conditions for ${C}_\mathrm{out}$, so that if ${C}_\mathrm{out}$ satisfies these, ${C}_\mathrm{out}\circ {C}_\mathrm{in}$ will likely lie on the GV bound. We hope that these conditions may inspire future work towards constructing explicit codes ${C}_\mathrm{out}$.

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arXiv2024

Random Reed-Solomon Codes are List Recoverable with Optimal List Size

We prove that Reed-Solomon (RS) codes with random evaluation points are list recoverable up to capacity with optimal output list size, for any input list size. Namely, given an input list size $\ell$, a designated rate $R$, and any $\varepsilon > 0$, we show that a random RS code is list recoverable from $1-R-\varepsilon$ fraction of errors with output list size $L = O(\ell/\varepsilon)$, for field size $q=\exp(\ell,1/\varepsilon) \cdot n^2$. In particular, this shows that random RS codes are list recoverable beyond the "list recovery Johnson bound". Such a result was not even known for arbitrary random linear codes. Our technique follows and extends the recent line of work on list decoding of random RS codes, specifically the works of Brakensiek, Gopi, and Makam (STOC 2023), and of Guo and Zhang (FOCS 2023).

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arXiv2020

Spectral Sparsification via Bounded-Independence Sampling

We give a deterministic, nearly logarithmic-space algorithm for mild spectral sparsification of undirected graphs. Given a weighted, undirected graph $G$ on $n$ vertices described by a binary string of length $N$, an integer $k\leq \log n$, and an error parameter $ε> 0$, our algorithm runs in space $\tilde{O}(k\log (N\cdot w_{\mathrm{max}}/w_{\mathrm{min}}))$ where $w_{\mathrm{max}}$ and $w_{\mathrm{min}}$ are the maximum and minimum edge weights in $G$, and produces a weighted graph $H$ with $\tilde{O}(n^{1+2/k}/ε^2)$ edges that spectrally approximates $G$, in the sense of Spielmen and Teng [ST04], up to an error of $ε$. Our algorithm is based on a new bounded-independence analysis of Spielman and Srivastava's effective resistance based edge sampling algorithm [SS08] and uses results from recent work on space-bounded Laplacian solvers [MRSV17]. In particular, we demonstrate an inherent tradeoff (via upper and lower bounds) between the amount of (bounded) independence used in the edge sampling algorithm, denoted by $k$ above, and the resulting sparsity that can be achieved.

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