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Protect your digital life with the latest privacy news, covering smart device surveillance risks and OS security backdoors.
Cloud agents typically send user messages to remote LLMs and store conversation traces in memory systems (e.g., Mem0, LangMem, Memobase) for long-term personalization. This creates a large privacy attack surface: plaintext prompts and logs may contain PII, medical/financial data, credentials cloud memory stores can leak via retrieval, prompt injection, inversion, or misconfiguration naïve mitigation (e.g., *** masking) destroys task semantics, harming retrieval and personalization Goal: reduce privacy leakage without sacrificing utility.
Paper page - MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents… The following papers were recommended by the Semantic Scholar API Privacy-Preserving Federated Action Recognition via Differentially Private Selective Tuning and Efficient Communication 2026 PAC-DP: Personalized Adaptive Clipping for Differentially Private Federated Learning 2026 DDP-SA: Scalable P… …
… Model Name Parameters Method HuggingFace Link 🤗 MemPrivacy-4B-RL 4B RL IAAR-Shanghai/MemPrivacy-4B-RL 🤗 MemPrivacy-4B-SFT 4B SFT IAAR-Shanghai/MemPrivacy-4B-SFT 🤗 MemPrivacy-1.7B-RL 1.7B RL IAAR-Shanghai/MemPrivacy-1.7B-RL 🤗 MemPrivacy-1.7B-SFT 1.7B SFT IAAR-Shanghai/MemPrivacy-1.7B-SFT Evaluation … …
… On three agentic benchmarks under strict privacy settings, PAAC dominates the Pareto frontier of privacy and accuracy, improving average accuracy by 15-36\% and reducing average leakage by 2-6times over state-of-the-art device-cloud baselines, with the largest margins on privacy targets outside fix… …
… AI-generated summary Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. …
… I built a privacy-first, cross-platform web UI that runs SmolVLM2-2.2B-Instruct vision alongside SmolLM3-3B text . …
… It's collaborative intelligence , where multiple independent LLMs across device and cloud exchange natural language or structured messages at the task level. 🧭 Two axes, one taxonomy We organize the design space along two composable dimensions: 📡 Vertical device-cloud collaboration : heuristic rout… …
… The following papers were recommended by the Semantic Scholar API Standing on the Shoulders of Giants: Stabilized Knowledge Distillation for Cross--Language Code Clone Detection 2026 XSearch: Explainable Code Search via Concept-to-Code Alignment 2026 CodeMMR: Bridging Natural Language, Code, and Im… …
… The hardest failures are often contextual: whether an action is acceptable depends on local privacy norms, organizational policies, and user expectations that resist pre-deployment specification. …
… Probing and Steering Contextual Privacy Norms in Large Language Model Representations 2026 Beyond Social Pressure: Benchmarking Epistemic Attack in Large Language Models 2026 Please give a thumbs up to this comment if you found it helpful! …
… Is there data privacy when doing this? …
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