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This work conducts a comprehensive investigation into determining the entropic hardness of (Ring/Module) Learning with Rounding (LWR) under polynomial modulus. Particularly, we establish the hardness of (M)LWR for general entropic secret distributions from (Module) LWE assumptions based on a new conceptually simple framework called rounding lossiness. By combining this hardness result and a trapdoor inversion algorithm with asymptotically the most compact parameters, we obtain a compact lossy trapdoor function (LTF) with improved efficiency. Extending our LTF with other techniques, we can derive a compact all-but-many LTF and PKE scheme against selective opening and chosen ciphertext attacks, solely based on (Module) LWE assumptions within a polynomial modulus. Additionally, we show a search-to-decision reduction for RLWR with Gaussian secrets from a new Rényi Divergence-based analysis.

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Revisiting the Robustness of (R/M)LWR Under Polynomial Moduli with Its Applications

  • 会议论文

  • 发表日期：

    2026年

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  • Zhedong Wang,
  • Haoxiang Jin,
  • Feng-Hao Liu,
  • Yang Yu

摘要

This work conducts a comprehensive investigation into determining the entropic hardness of (Ring/Module) Learning with Rounding (LWR) under polynomial modulus. Particularly, we establish the hardness of (M)LWR for general entropic secret distributions from (Module) LWE assumptions based on a new conceptually simple framework called rounding lossiness. By combining this hardness result and a trapdoor inversion algorithm with asymptotically the most compact parameters, we obtain a compact lossy trapdoor function (LTF) with improved efficiency. Extending our LTF with other techniques, we can derive a compact all-but-many LTF and PKE scheme against selective opening and chosen ciphertext attacks, solely based on (Module) LWE assumptions within a polynomial modulus. Additionally, we show a search-to-decision reduction for RLWR with Gaussian secrets from a new Rényi Divergence-based analysis.