Privacy-Preserving ML

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26 papers in the last four weeks, up 63% on the four weeks before. 0.3% of all new papers.

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Federated Computation of ROC and PR Curves
Oct 6, 2025Xuefeng Xu, Graham CormodePrivacy-Preserving ML Secure Aggregation
University of Warwick
Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning
Sep 26, 2025Li Xia, Jing Yu, Zheng Liu +3Gradient Inversion Attacks Adversarial Attacks
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China
Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies
Sep 13, 2025Yuping Wu, Viktor Schlegel, Warren Del-Pinto +11Medical Report Generation Healthcare
Univeristy of Manchester · Imperial College London, Imperial Global Singapore · Khoo Teck Puat Hospital, Singapore +2
Algorithmic Shortlisting in Participatory Budgeting
Aug 7, 2025Juan Ignacio Zambrano, Clément Contet, Jairo Gudiño-Rosero +3Learning to Rank Privacy-Preserving ML
IRIT · Université Toulouse Capitole · Université de Toulouse +3
High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
Aug 7, 2025Ziliang Shen, Caixing Wang, Shaoli Wang +1Quantile Regression Privacy-Preserving ML
School of Statistics and Data Science, Shanghai University of Finance and Economics · School of Statistics and Data Science, Southeast University · School of Statistics and Data Science, Shanghai University of International Business and Economics
Efficient Unlearning with Privacy Guarantees
Jul 7, 2025Josep Domingo-Ferrer, Najeeb Jebreel, David SánchezPrivacy-Preserving ML Machine Unlearning
1CYBERCAT (Center for Cybersecurity Research of2026 Catalonia), ComSCIAM-Center for Computational Science and Applied Mathematics, Department of Computer EngineeringJun and Mathematics, Universitat Rovira i Virgili, Av. Pa¨ısos26 Catalans 26, E-43007 Tarragona, Catalonia
Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction
Jun 18, 2025Vincent Roca, Marc Tommasi, Paul Andrey +8Brain Age Prediction Clinical Outcome Prediction
Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France · Univ. Lille, Inria, F-59000 Lille, France · Univ. Montpellier, Inria, F-34090 Montpellier, France +3
Private Rate-Constrained Optimization with Applications to Fair Learning
May 28, 2025Mohammad Yaghini, Tudor Cebere, Michael Menart +2Primal-Dual Optimization Group Fairness
University of Toronto · Vector Institute · PreMeDICaL team +3
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
May 28, 2025Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati +7Federated Learning Aggregation Differentially Private Stochastic Gradient Descent
German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany · Department of Computer Engineering, Bilkent University, Universiteler 06800 Çankaya/Ankara, Türkiye · Medical Research Group, MLCommons, San Francisco, CA, USA +2
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection
Apr 2, 2025Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo +5Network Intrusion Detection Encrypted Network Traffic Analysis
atlanTTic Research Center – ICLAB – Universidade de Vigo, Vigo, 36310, Spain · Department of Telematic Engineering, Universidad Carlos III de Madrid, Spain
From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification
Mar 15, 2025Yan Jiang, Hao Yu, Xu Cheng +3Privacy-Preserving ML Person Re-Identification
Center for Machine Vision and Signal Analysis, University of Oulu, Oulu FI-90014, Finland · School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
Mar 10, 2025Mukesh Sahani, Binanda SenguptaPrivacy-Preserving ML Distributed Training
Indian Institute of Technology (Indian School of Mines) Dhanbad, India · Indian Institute of Engineering Science and Technology, Shibpur, India
Batch effects can impair federated learning in multi-center omics studies
Dec 8, 2024Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann +5Secure Multi-Party Computation Privacy-Preserving ML
Institute for Computational Systems Biomedicine, University of Hamburg, 22761 Hamburg, Germany · Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany · Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark +3
PPFedIT: Towards Privacy-Preserving Federated Instruction Tuning with Few-shot Local Examples
Mar 10, 2024Zhuo Zhang, Jingyuan Zhang, Jintao Huang +6Privacy Leakage in Language Models Few-Shot Learning
Harbin Institute of Technology, Shenzhen, China · Peng Cheng Lab, Shenzhen, China · Kuaishou, Beijing, China +1
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
Jul 24, 2023Spencer Giddens, Yiwang Zhou, Kevin R. Krull +3Privacy-Preserving ML Differential Privacy
University of Notre Dame, Notre Dame, IN 46556, USA · St. Jude Children’s Research Hospital, Memphis, TN 38138, USA · University of Michigan, Ann Arbor, MI 48109, USA
Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
Jan 26, 2022John ChiangLogistic Regression Gradient Descent
Label Differential Privacy via Aggregation
Date pendingAnand Brahmbhatt, Rishi Saket, Shreyas Havaldar +3Privacy-Preserving ML Differential Privacy
Google Research India.
PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning
Date pendingMojtaba Taherisadr, Salma ElmalakiRL Control Cyber-Physical Systems
University of California, Irvine, USA
DNA: Differentially private Neural Augmentation for contact tracing
Date pendingRob Romijnders, Christos Louizos, Yuki M. Asano +1Privacy-Preserving ML Differential Privacy
University of Amsterdam · Qualcomm AI research
Membership Inference Attacks on Recommender System: A Survey
Date pendingJiajie He, Xintong Chen, Xinyang Fang +4Membership Inference Attacks Privacy-Preserving ML
Mcmaster University, Canada · Nanyang Technological University, SG

Oct 6, 2025, cs.LGJ/K move · Enter open · S save

Federated Computation of ROC and PR Curves

Xuefeng Xu, Graham Cormode

University of Warwick

Privacy-Preserving ML Secure Aggregation Federated Learning

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Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental analytical tools for evaluating classification models, providing a comprehensive view of performance across decision thresholds. In modern data management settings, such evaluation must often be performed over data that is distributed across multiple parties and subject to strict privacy constraints. This setting arises naturally in federated learning (FL) and collaborative data platforms, where raw prediction scores and labels cannot be centrally collected. We study the problem of computing ROC and PR curves over distributed data under formal privacy guarantees. The key challenge is that exact computation requires access to all prediction scores and labels, leading to linear communication costs and privacy risks. To address this, we propose a novel framework for approximating ROC and PR curves using quantiles of the score distribution, which can be computed efficiently under secure aggregation and distributed differential privacy. Our approach requires only

O(Q)

communication, where

Q

is the number of quantiles, and avoids sharing raw data entirely. We provide theoretical guarantees on the approximation quality by bounding the Area Error (AE) between the true and estimated curves, demonstrating principled trade-offs among approximation accuracy, privacy protection, and communication cost. Our method is robust to data heterogeneity and skew, making it suitable for real-world distributed data management scenarios. Experiments on real-world datasets show that our approach achieves high accuracy with low communication overhead under strong privacy constraints. Overall, this work provides a practical and theoretically grounded solution for privacy-preserving evaluation of classification models over distributed data.

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