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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
PM-Dedup: Secure Deduplication with Partial Migration fro...
Zhaokang Ke, Haoyu Gong, David H. C. Du · 2025-01-05 · via cs.CR updates on arXiv.org

Currently, an increasing number of users and enterprises are storing their data in the cloud but do not fully trust cloud providers with their data in plaintext form. To address this concern, they encrypt their data before uploading it to the cloud. However, encryption with different keys means that even identical data will become different ciphertexts, making deduplication less effective. Encrypted deduplication avoids this issue by ensuring that identical data chunks generate the same ciphertext with content-based keys, enabling the cloud to efficiently identify and remove duplicates even in encrypted form. Current encrypted data deduplication work can be classified into two types: target-based and source-based. Target-based encrypted deduplication requires clients to upload all encrypted chunks (the basic unit of deduplication) to the cloud with high network bandwidth overhead. Source-based deduplication involves clients uploading fingerprints (hashes) of encrypted chunks for duplicate checking and only uploading unique encrypted chunks, which reduces network transfer but introduces high latency and potential side-channel attacks, which need to be mitigated by Proof of Ownership (PoW), and high computing overhead of the cloud. So, reducing the latency and the overheads of network and cloud while ensuring security has become a significant challenge for secure data deduplication in cloud storage. In response to this challenge, we present PM-Dedup, a novel secure source-based deduplication approach that relocates a portion of the deduplication checking process and PoW tasks from the cloud to the trusted execution environments (TEEs) in the client-side edge servers. We also propose various designs to enhance the security and efficiency of data deduplication.