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Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets

arXiv:2607.08092v1 Announce Type: cross Abstract: Privacy-preserving clustering is critical for analyzing sensitive data in healthcare, cybersecurity, and enterprise applications, where maintaining da

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10 Jul 2026

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Performative Privacy: When Differential Privacy Maximizes Utility

EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy

Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning

Privacy Preserving Recommender Systems Balancing Personalization with Privacy

Rethinking Visual Privacy: A Compositional Privacy Risk Framework for Severity Assessment with VLMs

Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences

User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios

Lens Privacy Sealing: A New Benchmark and Method for Physical Privacy-Preserving Action Recognition

PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding

DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation

Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets

Robust Privacy: Inference-Stage Privacy through Certified Robustness

Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations

Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models

Privacy Washing: Detecting Internal Contradictions in Privacy Policies

Revisiting the Provable-Auditable Privacy Gap of DP-SGD

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy

Beyond Direct Identifiers: Probabilistic Privacy Risk Estimation for Privacy-Conscious LLM Query Delegation

Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs

PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems

Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling

Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy

See No Evil: Semantic Context-Aware Privacy Risk Detection for AR

Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy

Privacy-Aware Decoding: Mitigating Privacy Leakage of Large Language Models in Retrieval-Augmented Generation

Privacy Leakage via Output Label Space and Differentially Private Continual Learning

Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning

Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

Personalized Privacy Control in LLMs via Attention Head Intervention

Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View

PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior

When Privacy Hurts Mergeability: Geometry-Aware Model Merging under Differential Privacy

SoK: From Generation to Consumption of Privacy Documents in Software Systems

When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence

PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions

Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis

Theoretically Principled Federated Learning for Balancing Privacy and Utility

CutClean: Neural Network Pruning for Privacy-Preserving Inference

Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling

Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation

Limits of Personalizing Differential Privacy Budgets

Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy

Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access

Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks

Position: Privacy Is a Claim, Not a Property of Synthetic Data

Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models

LDP-Slicing: Local Differential Privacy for Images via Randomized Bit-Plane Slicing