Private Learning of Littlestone Classes, Revisited Xin Lyu http://arxiv.org/abs/2510.00076

Privately Estimating Black-Box Statistics Günter F. Steinke, Thomas Steinke http://arxiv.org/abs/2510.00322

Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings Bo Li, Wei Wang, Peng Ye http://arxiv.org/abs/2510.00574

Differentially Private Learning of Exponential Distributions: Adaptive Algorithms and Tight Bounds Bar Mahpud, Or Sheffet http://arxiv.org/abs/2510.00790

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation Aueaphum Aueawatthanaphisut http://arxiv.org/abs/2510.00976

Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognition Kassahun Azezew, Minyechil Alehegn, Tsega Asresa, Bitew Mekuria, Tizazu Bayh, Ayenew Kassie, Amsalu Tesema, Animut Embiyale http://arxiv.org/abs/2510.01113

Sample-Efficient Differentially Private Fine-Tuning via Gradient Matrix Denoising Ali Dadsetan, Frank Rudzicz http://arxiv.org/abs/2510.01137

SoftAdaClip: A Smooth Clipping Strategy for Fair and Private Model Training Dorsa Soleymani, Ali Dadsetan, Frank Rudzicz http://arxiv.org/abs/2510.01447

Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD Lea Demelius, Dominik Kowald, Simone Kopeinik, Roman Kern, Andreas Trügler http://arxiv.org/abs/2510.01744

Secure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP Aueaphum Aueawatthanaphisut http://arxiv.org/abs/2510.01780

Private Federated Multiclass Post-hoc Calibration Samuel Maddock, Graham Cormode, Carsten Maple http://arxiv.org/abs/2510.01987

Generating Differentially Private Networks with a Modified Erdős-Rényi Model Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti, Matthew Hale http://arxiv.org/abs/2509.25431

Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation Yiwei Li, Shuai Wang, Zhuojun Tian, Xiuhua Wang, Shijian Su http://arxiv.org/abs/2509.25906

Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression Haodong Liang, Yanhao Jin, Krishnakumar Balasubramanian, Lifeng Lai http://arxiv.org/abs/2509.22794

Copyright Infringement Detection in Text-to-Image Diffusion Models via Differential Privacy Xiafeng Man, Zhipeng Wei, Jingjing Chen http://arxiv.org/abs/2509.23022

DPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge Intelligence Yang Lv, Jin Cao, Ben Niu, Zhe Sun, Fengwei Wang, Fenghua Li, Hui Li http://arxiv.org/abs/2509.23030

CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy Zhanhong Xie, Meifan Zhang, Lihua Yin http://arxiv.org/abs/2509.23190

Adaptive Token-Weighted Differential Privacy for LLMs: Not All Tokens Require Equal Protection Manjiang Yu, Priyanka Singh, Xue Li, Yang Cao http://arxiv.org/abs/2509.23246

GPM: The Gaussian Pancake Mechanism for Planting Undetectable Backdoors in Differential Privacy Haochen Sun, Xi He http://arxiv.org/abs/2509.23834

Fundamental Limit of Discrete Distribution Estimation under Utility-Optimized Local Differential Privacy Sun-Moon Yoon, Hyun-Young Park, Seung-Hyun Nam, Si-Hyeon Lee http://arxiv.org/abs/2509.24173

Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks Amir Ziaeddini, Yauhen Yakimenka, Jörg Kliewer http://arxiv.org/abs/2509.21688

Federated Learning of Quantile Inference under Local Differential Privacy Leheng Cai, Qirui Hu, Shuyuan Wu http://arxiv.org/abs/2509.21800

Accuracy-First Rényi Differential Privacy and Post-Processing Immunity Ossi Räisä, Antti Koskela, Antti Honkela http://arxiv.org/abs/2509.22213

New Quantum Internet Applications via Verifiable One-Time Programs Lev Stambler http://arxiv.org/abs/2509.22290

Privacy Mechanism Design based on Empirical Distributions Leonhard Grosse, Sara Saeidian, Mikael Skoglund, Tobias J. Oechtering http://arxiv.org/abs/2509.22428

Differential Privacy of Network Parameters from a System Identification Perspective Andrew Campbell, Anna Scaglione, Hang Liu, Victor Elvira, Sean Peisert, Daniel Arnold http://arxiv.org/abs/2509.20460

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation Wenkai Guo, Xuefeng Liu, Haolin Wang, Jianwei Niu, Shaojie Tang, Jing Yuan http://arxiv.org/abs/2509.20680

Emerging Paradigms for Securing Federated Learning Systems Amr Akmal Abouelmagd, Amr Hilal http://arxiv.org/abs/2509.21147

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC Sahil Tyagi, Andrei Cozma, Olivera Kotevska, Feiyi Wang http://arxiv.org/abs/2509.19396

Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansion Puning Zhao, Zhikun Zhang, Bo Sun, Li Shen, Liang Zhang, Shaowei Wang, Zhe Liu http://arxiv.org/abs/2509.19661

Monitoring Violations of Differential Privacy over Time Önder Askin, Tim Kutta, Holger Dette http://arxiv.org/abs/2509.20283

VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks Efthymios Tsaprazlis, Thanathai Lertpetchpun, Tiantian Feng, Sai Praneeth Karimireddy, Shrikanth Narayanan http://arxiv.org/abs/2509.18413

Federated Learning for Financial Forecasting Manuel Noseda, Alberto De Luca, Lukas Von Briel, Nathan Lacour http://arxiv.org/abs/2509.16393

Federated Learning with Ad-hoc Adapter Insertions: The Case of Soft-Embeddings for Training Classifier-as-Retriever Marijan Fofonjka, Shahryar Zehtabi, Alireza Behtash, Tyler Mauer, David Stout http://arxiv.org/abs/2509.16508

Differential Privacy for Euclidean Jordan Algebra with Applications to Private Symmetric Cone Programming Zhao Song, Jianfei Xue, Lichen Zhang http://arxiv.org/abs/2509.16915

Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis Joshua Ward, Xiaofeng Lin, Chi-Hua Wang, Guang Cheng http://arxiv.org/abs/2509.18014

FedHK-MVFC: Federated Heat Kernel Multi-View Clustering Kristina P. Sinaga http://arxiv.org/abs/2509.15844

Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering Kristina P. Sinaga http://arxiv.org/abs/2509.16101

Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph Gautam Kamath, Alireza F. Pour, Matthew Regehr, David P. Woodruff http://arxiv.org/abs/2509.16180

FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health Nobin Sarwar, Shubhashis Roy Dipta http://arxiv.org/abs/2509.14275

Normalized Square Root: Sharper Matrix Factorization Bounds for Differentially Private Continual Counting Monika Henzinger, Nikita P. Kalinin, Jalaj Upadhyay http://arxiv.org/abs/2509.14334

SynBench: A Benchmark for Differentially Private Text Generation Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Iqra Zahid, Yuping Wu, Yulong Wu, Hao Li, Jie Zhang, Warren Del-Pinto, Goran Nenadic, Siew Kei Lam, Anil Anthony Bharath http://arxiv.org/abs/2509.14594

Practitioners' Perspectives on a Differential Privacy Deployment Registry Priyanka Nanayakkara, Elena Ghazi, Salil Vadhan http://arxiv.org/abs/2509.13509

Privacy-Aware In-Context Learning for Large Language Models Bishnu Bhusal, Manoj Acharya, Ramneet Kaur, Colin Samplawski, Anirban Roy, Adam D. Cobb, Rohit Chadha, Susmit Jha http://arxiv.org/abs/2509.13625

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning Zihou Wu, Yuecheng Li, Tianchi Liao, Jian Lou, Chuan Chen http://arxiv.org/abs/2509.13739

Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearning Zhaoyang Chu, Yao Wan, Zhikun Zhang, Di Wang, Zhou Yang, Hongyu Zhang, Pan Zhou, Xuanhua Shi, Hai Jin, David Lo http://arxiv.org/abs/2509.13755

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response Ozer Ozturk, Busra Buyuktanir, Gozde Karatas Baydogmus, Kazim Yildiz http://arxiv.org/abs/2509.13987

Differentially private federated learning for localized control of infectious disease dynamics Raouf Kerkouche, Henrik Zunker, Mario Fritz, Martin J. Kühn http://arxiv.org/abs/2509.14024

Piquant$ varepsilon$: Private Quantile Estimation in the Two-Server Model Hannah Keller, Jacob Imola, Fabrizio Boninsegna, Rasmus Pagh, Amrita Roy Chowdhury http://arxiv.org/abs/2509.14035

Auditable Early Stopping for Agentic Routing: Ledger-Verified Run-Wise Certificates under Local DP Shivam Akhauri http://arxiv.org/abs/2509.10550