The Maximum Nonlinearity of 8-variable Balanced Boolean Functions is 116
Does there exist an 8-variable balanced Boolean function with nonlinearity 118? This is a famous and long-standing open
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共收录 102 条相关情报。
← 返回所有主题Can Rényi Accountants Beat Lossless Accountants for DP-SGD?
Recent frameworks in differential privacy (DP), such as privacy loss distributions and $f$-DP, provide lossless represen
The Right to Be Forgotten under Correlation: Privacy-Utility Tradeoffs
We study privacy-preserving algorithms that redact information from a correlated dataset. The goal is to reduce the info
Digital-Twin-Referenced Belief-Guided Q-Learning for Jammer Identification and Beam-Power Control in a mmWave Model
Digital Twin (DT) combined with reinforcement learning (RL) can support adaptive resource allocation and security-aware
Improved Local Leakage Resilience of Shamir Secret Sharing and Worst-Case Optimal Polynomial Intersection
We study two problems: Local Leakage Resilience (LLR) for Shamir secret sharing, and worst-case Optimal Polynomial Inter
Verification with Transfer: Exact Information Frontiers and Their Price in Calls
A verifier that accepts or rejects whole answers reveals little: under a flat prior over $k$-bit answers, zero error nee
Language Modeling is Monotone Compression
A long-standing hypothesis in artificial intelligence and neuroscience posits that intelligence is closely related to co
The Price of Privacy: Randomness Complexity of Graph-Based Multi-Secret Sharing
We study the randomness required to share possibly correlated secret bits among parties connected by a graph. A dealer p
Cryptanalysis of a Class of Ideal Secret Sharing Schemes Based on the CRT for Polynomial Rings
Based on the CRT for polynomial rings, Yang, Zhu, Fu and Xia (ISIT 2026) proposed a compartmented secret sharing scheme
SimpleMark: Fast Multi-Bit Text Watermarking under f -Divergence Constraints
We introduce a framework for multi-bit text watermarking with security defined directly through $f$-divergence from the
Unifying Privacy Accounting: Information Equivalence and Information Loss
论文针对差分隐私（DP）中多种曲线型隐私定义并存、选择不同定义会影响隐私分析与效用的问题，提出统一的信息论框架。作者考虑四类主流定义：两个方向性的 (ε,δ)-DP 隐私轮廓、假设检验权衡函数对，以及扩展隐私损失分布。对固定输出分布的有序对
Intent-Hiding Jailbreaks: An Information-Theoretic Framework for Compositional Attacks
近期研究表明，大语言模型可被“组合式”越狱攻击：有害意图与若干看似无害的任务被拼接进同一条查询，单独的恶意请求会被拒绝，但嵌入更大规模的良性查询后却可能获得回应。该论文从信息论视角系统刻画这类“意图隐藏”越狱。作者为每个任务赋予一个“被判定
Quantum Advantage for Two-Party Differential Privacy
本文研究两方差分隐私（two-party differential privacy）问题：双方各持一段长度为 n 的比特串，需要联合计算二者的汉明距离，并且双方必须输出相同的估计值。作者关注的是信息论意义（而非计算假设意义）下的隐私保证。经
Learnt Attacks on Quantum Key Distribution under Channel Noise and Device Drift
该论文研究量子密钥分发（QKD）链路在非平稳信道条件下的安全性缺口。传统 QKD 安全分析基于「信道平稳」假设，而实际设备在两次校准之间会发生漂移（device drift），信道参数随时间变化。作者提出并量化了一个此前未被回答的问题：一个
Progressive-Resolution Secure Aggregation for Federated Learning
该论文聚焦联邦学习（FL）中的安全聚合（Secure Aggregation）机制，并指出其一个长期被忽视的功能性局限：在传统协议中，聚合结果的精度（分辨率）在客户端上传更新时就被一次性固定下来。也就是说，服务器与客户端在协议开始前必须就量
On the Relationship between Model Quantization and Model Inversion Attacks
该论文研究模型量化与模型反演攻击（Model Inversion, MI）之间的定量关系，并据此提出一种兼顾隐私与效用的训练后量化方法。研究背景是：量化通过降低神经网络权重与激活的数值精度来压缩存储与计算开销，已在边缘与部署场景广泛使用；但
Contraction and Statistical Inference under Privacy for Uniformly Bounded Distributions
本文研究 c-内点点态最大泄露（c-interior pointwise maximal leakage, PML）作为收缩性分析与披露控制工具的理论价值。传统局部差分隐私（LDP）要求对任意相邻输入，输出核满足有界似然比，这在实际系统中往
Feedback Coding Enables Inference-Time Covert Agentic Communication
该论文研究大语言模型（LLM）驱动的智能体在看似正常的对话中隐藏信息传输（隐写/隐蔽信道）的问题。作者指出，现有 LLM 隐写方案大多是白盒的：收发双方必须共享载体（cover）统计信息，通常意味着要能访问模型权重与提示词，部署门槛高；已有
Low-Rank Masking for Single-Server Matrix Multiplication
本文研究在有限域 F_q 上把矩阵乘法外包给单一（不可信）服务器时，采用秩不超过 r 的加性掩码能提供多强的统计隐私。针对独立均匀的 n×n 输入矩阵，作者证明两类自然采样方式——均匀 rank-ball 掩码，以及独立均匀因子的乘积——对
Intrinsic-Dimensional Wasserstein Guarantees for Private Synthetic Measures
该论文研究的是差分隐私合成测度（differentially private synthetic measure）的效用上界问题，属于差分隐私与最优传输/高维统计交叉的理论工作。作者考虑最坏情况数据模型：给定 [0,1]^d 空间中的 n