International Association
for Cryptologic Research

We consider the Learning Parity with Noise (LPN) problem with a sparse secret, where the secret vector $\mathbf{s}$ of dimension $n$ has Hamming weight at most $k$. We are interested in algorithms with asymptotic improvement in the \emph{exponent} beyond the state of the art. Prior work in this setting presented algorithms with runtime $n^{c \cdot k}$ for constant $c < 1$, obtaining a constant factor improvement over brute force search, which runs in time ${n \choose k}$. We obtain the following results: - We first consider the \emph{constant} error rate setting, and in this case present a new algorithm that leverages a subroutine from the acclaimed BKW algorithm [Blum, Kalai, Wasserman, J.~ACM '03] as well as techniques from Fourier analysis for $p$-biased distributions. Our algorithm achieves asymptotic improvement in the exponent compared to prior work, when the sparsity $k = k(n) = \frac{n}{\log^{1+ 1/c}(n)}$, where $c \in o(\log \log(n))$ and $c \in \omega(1)$. The runtime and sample complexity of this algorithm are approximately the same. - We next consider the \emph{low noise} setting, where the error is subconstant. We present a new algorithm in this setting that requires only a \emph{polynomial} number of samples and achieves asymptotic improvement in the exponent compared to prior work, when the sparsity $k = \frac{1}{\eta} \cdot \frac{\log(n)}{\log(f(n))}$ and noise rate of $\eta \neq 1/2$ and $\eta^2 = \left(\frac{\log(n)}{n} \cdot f(n)\right)$, for $f(n) \in \omega(1) \cap n^{o(1)}$. To obtain the improvement in sample complexity, we create subsets of samples using the \emph{design} of Nisan and Wigderson [J.~Comput.~Syst.~Sci. '94], so that any two subsets have a small intersection, while the number of subsets is large. Each of these subsets is used to generate a single $p$-biased sample for the Fourier analysis step. We then show that this allows us to bound the covariance of pairs of samples, which is sufficient for the Fourier analysis. - Finally, we show that our first algorithm extends to the setting where the noise rate is very high $1/2 - o(1)$, and in this case can be used as a subroutine to obtain new algorithms for learning DNFs and Juntas. Our algorithms achieve asymptotic improvement in the exponent for certain regimes. For DNFs of size $s$ with approximation factor $\epsilon$ this regime is when $\log \frac{s}{\epsilon} \in \omega \left( \frac{c}{\log n \log \log c}\right)$, and $\log \frac{s}{\epsilon} \in n^{1 - o(1)}$, for $c \in n^{1 - o(1)}$. For Juntas of $k$ the regime is when $k \in \omega \left( \frac{c}{\log n \log \log c}\right)$, and $k \in n^{1 - o(1)}$, for $c \in n^{1 - o(1)}$.

In a recent result, Dachman-Soled et al. (TCC ’15) proposed a new notion called locally decodable and updatable non-malleable codes, which informally, provides the security guarantees of a non-malleable code while also allowing for efficient random access. They also considered locally decodable and updatable non-malleable codes that are leakage-resilient, allowing for adversaries who continually leak information in addition to tampering.The bounded retrieval model (BRM) (cf. Alwen et al. (CRYPTO ’09) and Alwen et al. (EUROCRYPT ’10)) has been studied extensively in the setting of leakage resilience for cryptographic primitives. This threat model assumes that an attacker can learn information about the secret key, subject only to the constraint that the overall amount of leaked information is upper bounded by some value. The goal is then to construct cryptosystems whose secret key length grows with the amount of leakage, but whose runtime (assuming random access to the secret key) is independent of the leakage amount.In this work, we combine the above two notions and construct local non-malleable codes in the split-state model, that are secure against bounded retrieval adversaries. Specifically, given leakage parameter $$\ell $$ℓ, we show how to construct an efficient, 3-split-state, locally decodable and updatable code (with CRS) that is secure against one-time leakage of any polynomial time, 3-split-state leakage function whose output length is at most $$\ell $$ℓ, and one-time tampering via any polynomial-time 3-split-state tampering function. The locality we achieve is polylogarithmic in the security parameter.