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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
Order-Preserving Database Encryption with Secret Sharing
Dongfang Zhao · 2023-01-11 · via cs.CR updates on arXiv.org

The order-preserving encryption (OPE) problem was initially formulated by the database community in 2004 soon after the paradigm database-as-a-service (DaaS) was coined in 2002. Over the past two decades, OPE has drawn tremendous research interest from communities of databases, cryptography, and security; we have witnessed significant advances in OPE schemes both theoretically and systematically. All existing OPE schemes assume that the outsourced database is modeled as a single semi-honest adversary who should learn nothing more than the order information of plaintext messages up to a negligible probability. This paper addresses the OPE problem from a new perspective: instead of modeling the outsourced database as a single semi-honest adversary, we assume the outsourced database \textit{service} compromises a cluster of non-colluding servers, which is a practical assumption as all major cloud vendors support multiple database instances deployed to exclusive sub-networks or even to distinct data centers. This assumption allows us to design a new stateless OPE protocol, namely order-preserving database encryption with secret sharing (ODES), by employing secret-sharing schemes among those presumably non-colluding servers. We will demonstrate that ODES guarantees the latest security level, namely IND-FAOCPA, and outperforms the state-of-the-art scheme by orders of magnitude.