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Differential Privacy Meets Fixed Parameter Tractability: Algorithms and Lower Bounds
Theory paper combines differential privacy with fixed-parameter tractable encoders, improving approximation guarantees for combinatorial optimization and proving new lower bounds.
The paper studies combinatorial optimization under epsilon-differential privacy within the implicit encoder-decoder framework of Gupta et al. (SODA 2010), generalizing it to allow fixed-parameter tractable encoders. This circumvents approximation barriers inherent to polynomial-time algorithms and yields improved guarantees for fundamental combinatorial optimization problems. The authors establish the first representation-independent lower bounds: assuming a non-uniform variant of the Gap Exponential Time Hypothesis, no epsilon-DP encoder-decoder pair can achieve certain approximation guarantees with a subexponential-time decoder for sufficiently small epsilon. Representation-dependent lower bounds are also provided for larger epsilon.
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SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing
SpliTEE splits LLM inference between Intel TDX trusted execution and untrusted GPUs, using differential privacy instead of encryption to protect intermediate representations.
SpliTEE extends split inference to LLMs, running inference partly inside an Intel TDX TEE while masking intermediate inputs sent to untrusted GPUs with differential privacy rather than encryption. The authors show a prompt-reconstruction attack recovers nearly 80% of prompts from unmasked intermediate representations, motivating the masking. A global sensitivity analysis bounds the required DP noise scale, avoiding quantization and keeping models in floating point. The implementation is nearly twice as fast as full CPU-based TDX inference and 5-15 seconds faster than encryption-based Slalom with higher accuracy, evaluated on Llama-3.2-3B and Qwen3-4B.
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Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation
Federated learning framework combining dynamic differential privacy, homomorphic encryption, and local DP retains 82.6% accuracy at epsilon 0.1 while cutting communication 21.3%.
The paper proposes a privacy-enhanced federated learning framework integrating Dynamic Differential Privacy, lightweight Homomorphic Encryption, and Local Differential Privacy during training. An asynchronous aggregation strategy with version control supports distributed training in asynchronous environments. On CIFAR-10 and Purchase-100, the method maintains up to 82.6% classification accuracy under stringent privacy constraints (epsilon = 0.1) and reduces communication overhead by 21.3% versus FedAvg.
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Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology
Case study applies Gaussian and Laplace differential privacy to clinical EEG features, quantifying privacy-utility trade-offs across three deployment scenarios.
Researchers evaluate subject-level differential privacy for EEG-derived feature representations using Gaussian and Laplace perturbations across client-side, centralized server-side, and decentralized local training scenarios. Utility is assessed with statistical measures and a downstream machine-learning check on clinical neurophysiology data. Results show DP can be integrated into EEG workflows, but mechanism choice, privacy parameters, and sensitivity calibration strongly influence data utility, particularly on small and imbalanced clinical datasets. The study highlights the privacy-utility trade-off in protecting biomedical signals against re-identification and inference risks.
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Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication
Paper releases a private client context once and confines adaptation to coefficients, matching full-model differential privacy with 2.67x less uplink on CIFAR-10.
The paper addresses the dimensionality misalignment between record-level differential privacy and low-dimensional client variation in personalized federated learning by releasing a private client context once and restricting repeated adaptation to a fixed coefficient space. A variable-length quantized Gaussian mechanism lets quantization error itself serve as the required privacy perturbation. On MNIST and CIFAR-10, the design matches or outperforms full-model private adaptation across privacy budgets and client heterogeneity while cutting protected uplink 2.67x at epsilon=16 on CIFAR-10.
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RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments
RegionFed is a gradient-level federated learning framework enabling personalized retail query understanding while matching centralized accuracy with differential privacy.
RegionFed is an architecture-robust federated learning framework for personalized query understanding that operates at the gradient level, using the l2 conflict between regional and global gradients to diagnose heterogeneity and control personalization. Existing parameter-level personalized FL methods collapse on transformers, falling below 10% accuracy on T5, while RegionFed deploys unchanged on T5-Small, T5-3B, RoBERTa, and CNNs. RegionFed-Meta achieves 92.27% across Amazon ESCI, Amazon Reviews, and LEAF-FEMNIST, within 0.23 percentage points of the centralized upper bound, with epsilon-approx-0.60 differential privacy.
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Chinese AI firms are siphoning capabilities from American models, CISA warns
CISA, NSA and FBI warn Chinese AI firms including DeepSeek and Moonshot AI extracted billions of tokens from US frontier models via distillation campaigns.
A joint CISA, NSA and FBI advisory says China-based firms including DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun and Z.AI have run industrial-scale knowledge distillation campaigns against US frontier models such as Claude, GPT, Gemini and Grok since at least late 2024, likely with Chinese government knowledge. The campaigns used gray-market API proxies called transfer stations, premium account pools and traffic routing to bypass restrictions, evade safeguards and exfiltrate billions of tokens used to train models like DeepSeek R1/V3 and Kimi. The agencies challenge DeepSeek's reported $5.6 million training cost and recommend identity verification, usage monitoring, response variation, differential privacy and indicator sharing as defenses.
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Week in review: Critical VMware vCenter Server bugs fixed, Apple releases iOS 18
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Subgroup Membership Inference Audits of Differentially Private Synthetic Text
Audits of 32 differentially private synthetic-text releases show subgroup membership leakage is concentrated in few records and systematically underestimated by average-case attacks.
The paper defines a subgroup-targeted membership inference game in which the target pool is an explicit parameter, to audit residual leakage in differentially private synthetic text releases. The audit instantiates 32 proxies across four datasets, three generators (DP-SGD fine-tuning, API-based prompting, and activation steering), and five privacy budgets. DP substantially reduces average leakage at every budget, but remaining leakage is concentrated: roughly a tenth of records carries about 40% of it, and the noise removes more measured leakage from random records than from high-risk ones. Which records leak depends on the release mechanism, so record-level risk cannot be assessed independently of the release.
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