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Revisiting the attacker's knowledge in inference attacks against Searchable Symmetric Encryption

Apr 14, 2025

This work investigates the dependence of inference attacks in Searchable Symmetric Encryption (SSE) on the quality of “similar data” available to the adversary. We propose the first general statistical analysis framework that formally defines “similar data” and reveals how its non-uniqueness critically impacts attack robustness. We prove that index size constraints significantly degrade inference attack efficacy and derive a provably secure lower bound on the required index size. Within the leakage-abuse model, we integrate probabilistic modeling with statistical estimation theory and empirically validate our findings on the Enron dataset: imposing an index size cap of 200 reduces the optimal inference attack’s accuracy to below 5% with high probability. Our results yield the first quantifiable, data-similarity-aware defense configuration guideline for SSE systems—bridging theoretical security guarantees with practical deployment constraints.

1 citationsRead paper

Scaling-Score Conformal Prediction for Multi-Target Regression

Sep 15, 2026

本文提出了一种解决多目标回归中联合覆盖问题的新方法——缩放分数保形预测，该方法模型无关且仅需单一校准集。

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From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support

Aug 18, 2026

本文提出SC2R框架，通过结合预测模型、整数规划和语义约束等方法，为教育决策提供可行的干预方案，解决学生风险预测后缺乏具体可行干预措施的问题。

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Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers

Jul 30, 2026

This work addresses the sensitivity to initial guesses and high computational cost of Newton’s method for solving nonlinear parameterized partial differential equations. The authors propose a two-stage initialization strategy: first, by leveraging parameter sampling and a precomputed solution library, they construct two complementary feature spaces—solution manifold and corrected search directions—from discrete Newton trajectories; second, a regression model predicts a surrogate initial guess, which is then refined via lightweight GMRES-based residual minimization to yield a high-quality starting point. Operating under a weakly intrusive framework, this approach significantly accelerates high-fidelity Newton iterations, markedly reducing both iteration counts and total CPU time on benchmark PDE problems, outperforming existing methods that rely solely on surrogate-based initialization.

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Dependent Censoring Based on Geometric Optimization

Jun 15, 2026

This study addresses the estimation bias in survival analysis arising from dependence between failure and censoring times by proposing a novel framework based on the extended generalized Marshall–Olkin (EGMO) model. For the first time, geometric optimization techniques are integrated into dependent censoring modeling to effectively capture the underlying dependence structure. The proposed method combines theoretical rigor with computational efficiency and establishes the asymptotic properties of both parameter estimators and survival function estimators. Extensive simulations and real-data analyses demonstrate that the approach achieves superior robustness and estimation accuracy across a variety of dependent censoring scenarios.

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Latest Papers

Scaling-Score Conformal Prediction for Multi-Target Regression

Sep 15, 2026

本文提出了一种解决多目标回归中联合覆盖问题的新方法——缩放分数保形预测，该方法模型无关且仅需单一校准集。

0 citationsRead paper

From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support

Aug 18, 2026

本文提出SC2R框架，通过结合预测模型、整数规划和语义约束等方法，为教育决策提供可行的干预方案，解决学生风险预测后缺乏具体可行干预措施的问题。

0 citationsRead paper

Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers

Jul 30, 2026

This work addresses the sensitivity to initial guesses and high computational cost of Newton’s method for solving nonlinear parameterized partial differential equations. The authors propose a two-stage initialization strategy: first, by leveraging parameter sampling and a precomputed solution library, they construct two complementary feature spaces—solution manifold and corrected search directions—from discrete Newton trajectories; second, a regression model predicts a surrogate initial guess, which is then refined via lightweight GMRES-based residual minimization to yield a high-quality starting point. Operating under a weakly intrusive framework, this approach significantly accelerates high-fidelity Newton iterations, markedly reducing both iteration counts and total CPU time on benchmark PDE problems, outperforming existing methods that rely solely on surrogate-based initialization.

0 citationsRead paper

Dependent Censoring Based on Geometric Optimization

Jun 15, 2026

This study addresses the estimation bias in survival analysis arising from dependence between failure and censoring times by proposing a novel framework based on the extended generalized Marshall–Olkin (EGMO) model. For the first time, geometric optimization techniques are integrated into dependent censoring modeling to effectively capture the underlying dependence structure. The proposed method combines theoretical rigor with computational efficiency and establishes the asymptotic properties of both parameter estimators and survival function estimators. Extensive simulations and real-data analyses demonstrate that the approach achieves superior robustness and estimation accuracy across a variety of dependent censoring scenarios.

0 citationsRead paper

SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors

Jun 09, 2026

This work addresses the misalignment between model training objectives and the goal of producing efficient (i.e., narrow) prediction intervals in traditional conformal prediction (CP), which is typically applied as a post-hoc procedure. The authors propose SPACR, a novel method that, for the first time, jointly optimizes both validity and efficiency of prediction intervals within a single end-to-end training process via a differentiable loss function—eliminating the need for data splitting or pre-specifying multiple confidence levels. SPACR enables a single model to output valid prediction intervals simultaneously across multiple confidence levels, avoiding the repeated training required by approaches like DOICR and thereby substantially improving computational efficiency and interval sharpness. Experiments demonstrate that SPACR consistently yields narrower yet well-calibrated intervals across diverse datasets, achieving a superior trade-off among coverage accuracy, efficiency, and computational cost.

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