惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
爱范儿
爱范儿
博客园 - 三生石上(FineUI控件)
Vercel News
Vercel News
M
MIT News - Artificial intelligence
L
LangChain Blog
大猫的无限游戏
大猫的无限游戏
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Microsoft Azure Blog
Microsoft Azure Blog
J
Java Code Geeks
Recent Announcements
Recent Announcements
Stack Overflow Blog
Stack Overflow Blog
人人都是产品经理
人人都是产品经理
IT之家
IT之家
F
Fortinet All Blogs
博客园 - 聂微东
U
Unit 42
Martin Fowler
Martin Fowler
腾讯CDC
博客园_首页
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
量子位
阮一峰的网络日志
阮一峰的网络日志
博客园 - Franky

cs.CR updates on arXiv.org

Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
FHE-SQL: Fully Homomorphic Encrypted SQL Database
Po-Yu Tseng, Po-Chu Hsu, Shih-Wei Liao · 2025-10-17 · via cs.CR updates on arXiv.org

FHE-SQL is a privacy-preserving database system that enables secure query processing on encrypted data using Fully Homomorphic Encryption (FHE), providing privacy guaranties where an untrusted server can execute encrypted queries without learning either the query contents or the underlying data. Unlike property-preserving encryption-based systems such as CryptDB, which rely on deterministic or order-preserving encryption and are vulnerable to frequency, order, and equality-pattern inference attacks, FHE-SQL performs computations entirely under encryption, eliminating these leakage channels. Compared to trusted-hardware approaches such as TrustedDB, which depend on a hardware security module and thus inherit its trust and side-channel limitations, our design achieves end-to-end cryptographic protection without requiring trusted execution environments. In contrast to high-performance FHE-based engines-Hermes, which target specialized workloads such as vector search, FHE-SQL supports general SQL query semantics with schema-aware, type-safe definitions suitable for relational data management. FHE-SQL mitigates the high cost of ciphertext space by using an indirection architecture that separates metadata in RocksDB from large ciphertexts in blob storage. It supports oblivious selection via homomorphic boolean masks, multi-tier caching, and garbage collection, with security proven under the Universal Composability framework.