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        <identifier>oai:drops-oai.dagstuhl.de:12588</identifier>
        <datestamp>2024-03-06T10:50:31Z</datestamp>
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          <dc:title>Hardness of Bounded Distance Decoding on Lattices in 𝓁_p Norms</dc:title>
          <dc:creator>Bennett, Huck</dc:creator>
          <dc:creator>Peikert, Chris</dc:creator>
          <dc:subject>Lattices</dc:subject>
          <dc:subject>Bounded Distance Decoding</dc:subject>
          <dc:subject>NP-hardness</dc:subject>
          <dc:subject>Fine-Grained Complexity</dc:subject>
          <dc:description>Bounded Distance Decoding BDD_{p,α} is the problem of decoding a lattice when the target point is promised to be within an α factor of the minimum distance of the lattice, in the 𝓁_p norm. We prove that BDD_{p, α} is NP-hard under randomized reductions where α → 1/2 as p → ∞ (and for α = 1/2 when p = ∞), thereby showing the hardness of decoding for distances approaching the unique-decoding radius for large p. We also show fine-grained hardness for BDD_{p,α}. For example, we prove that for all p ∈ [1,∞) ⧵ 2ℤ and constants C &gt; 1, ε &gt; 0, there is no 2^((1-ε)n/C)-time algorithm for BDD_{p,α} for some constant α (which approaches 1/2 as p → ∞), assuming the randomized Strong Exponential Time Hypothesis (SETH). Moreover, essentially all of our results also hold (under analogous non-uniform assumptions) for BDD with preprocessing, in which unbounded precomputation can be applied to the lattice before the target is available.&#13;
Compared to prior work on the hardness of BDD_{p,α} by Liu, Lyubashevsky, and Micciancio (APPROX-RANDOM 2008), our results improve the values of α for which the problem is known to be NP-hard for all p &gt; p₁ ≈ 4.2773, and give the very first fine-grained hardness for BDD (in any norm). Our reductions rely on a special family of "locally dense" lattices in 𝓁_p norms, which we construct by modifying the integer-lattice sparsification technique of Aggarwal and Stephens-Davidowitz (STOC 2018).</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Huck Bennett and Chris Peikert</dc:contributor>
          <dc:date>2020</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 169, 35th Computational Complexity Conference (CCC 2020)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.CCC.2020.36</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-125881</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2020.36</dc:identifier>
          <dc:language>eng</dc:language>
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