Privacy-Preserving ML

ML: Machine Learning

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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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Encrypted Neural Networks without Overflows
May 21, 2026Philipp Kern, Lorenzo Rovida, Samuel Teuber +3Neural Network Verification Homomorphic Encryption
Karlsruhe Institute of Technology, Germany · Polytechnic University of Turin, Italy · The University of Manchester, UK +2
FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
May 21, 2026Maryam Moradpour, Jonas Harriehausen, Amirreza Aleyasin +3Federated Learning Aggregation Cancer Survival Prediction
Institute for Predictive Deep Learning in Medicine and Healthcare, Justus Liebig University Gießen, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Darmstadt, Germany · Department of Medical Informatics, University Medical Center Göttingen, Germany +1
Building a privacy-preserving Federated Recommender system for mobile devices
May 21, 2026Aasheesh SinghPrivacy-Preserving ML Recommender Systems
Université de Montréal · Département d’informatique et de recherche opérationnelle · Faculté des arts et des sciences
Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
May 21, 2026Christian Janos Lebeda, David Erb, Tudor Cebere +1Privacy-Preserving ML Differential Privacy
PreMeDICaL, Inria, Université de Montpellier, INSERM, France · Technical University of Munich, Germany · Work done while visiting PreMeDICaL, Inria.
FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning
May 21, 2026Rachid HedjamFederated Learning Aggregation Non-IID Federated Learning
Department of Computer Science Bishop’s University Sherbrooke, Qc, Canada
An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
May 19, 2026Hoang Tran, Jorge Ramirez, Jiayi Wang +3Fine-Tuning Privacy-Preserving ML
Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA · Departamento de Matem´aticas, Universidad Nacional de Colombia, Medell´ın, Colombia. · Computational Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA +1
Inferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies
May 19, 2026Yasmine HayderGraph Representation Learning Adversarial Attacks
PETSCRAFT · LIFO, INSA CVL, Univ. Orléans, Inria, France
Lens Privacy Sealing: A New Benchmark and Method for Physical Privacy-Preserving Action Recognition
May 19, 2026Mengyuan Liu, Ziyi Wang, Peiming Li +1Video Action Recognition Privacy-Preserving ML
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School · Department of Computer Science and Engineering, State University of New York at Buffalo
FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
May 18, 2026Nuredin Ali Abdelkadir, Anjali Ratnam, Zeerak Talat +1Depression Detection Mental Health
University of Minnesota · University of Edinburgh
Privacy Preserving Reinforcement Learning with One-Sided Feedback
May 18, 2026Lin William Cong, Guangyan Gan, Hanzhang Qin +1Reinforcement Learning Partially Observable RL
1Nanyang Technological University · Cornell SC Johnson College of Business · 2National University of Singapore
Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets
May 18, 2026Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti +8Healthcare Synthetic Tabular Data Generation
University of California, Irvine, CA, USA · PathAI, USA
On Applicability of Synthetic Datasets for Facial Expression Recognition
May 17, 2026Ali Azmoudeh, Erdi Sarıtaş, Ömer Yıldırım +1Class-Imbalanced Learning Synthetic Data Generation
Department of Computer Engineering, Istanbul Technical University, Istanbul, Türkiye · Department of Informatics, University of Zurich, Zurich, Switzerland · Division of Engineering, NYU Abu Dhabi, Abu Dhabi, UAE
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
May 17, 2026Haichao Sha, Zihao Wang, Yuncheng Wu +2Fine-Tuning Privacy-Preserving Language Models
Renmin University of China · Nanyang Technological University
Thermal-Only Crowd Counting with Deployment-Time Privacy Protection
May 16, 2026Yifei Qian, Zhongliang Guo, Chun Tong Lei +4Thermal Imaging Crowd Counting
School of Computer Science, University of Nottingham, NG7 2RD Nottingham, U.K. · School of Computer Science, University of St Andrews, KY16 9AJ St Andrews, U.K. · Department of Data Science, City University of Hong Kong, Hong Kong SAR, China +1
Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework
May 16, 2026Xiaolu Chen, Chenghao Huang, Yanru Zhang +1Privacy-Preserving ML Federated Learning
School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu, China · Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, VIC 3800, Australia · Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University, Melbourne, VIC 3800, Australia +1
Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy
May 16, 2026Youngmok Ha, Viktor Schlegel, Yidan Sun +1Privacy-Preserving ML Differential Privacy
Department of Bioengineering, Imperial College London, United Kingdom · Artificial Intelligence Computing Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea · Imperial Global Singapore, Imperial College London, Singapore +1
Public-Decay Homomorphic State Space Models for Private Sequence Inference
May 15, 2026Luis BritoEfficient Neural Network Inference Homomorphic Encryption
School of Technology and Management (ESTG-IPVC) Polytechnic Institute of Viana do Castelo, Viana do Castelo, Portugal
Differentially Private Motif-Preserving Multi-modal Hashing
May 14, 2026Zehua Cheng, Wei Dai, Jiahao SunCross-Modal Learning Cross-Modal Retrieval
Department of Computer Science University of Oxford Oxford, United Kingdom · FLock.io London, United Kingdom
MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification
May 14, 2026Weisen Jiang, Shuhao Chen, Sinno Jialin PanPrivacy-Preserving ML Mixture-of-Experts Models
Department of Computer Science and Engineering, The Chinese University of Hong Kong · Department of Computer Science and Engineering, Hong Kong University of Science and Technology · Department of Computer Science and Engineering, Southern University of Science and Technology
Keyed Nonlinear Transform: Lightweight Privacy-Enhancing Feature Sharing for Medical Image Analysis
May 13, 2026Haebom Lee, Gyeongjung KimMedical Image Analysis Split Inference
OOLU Soft Co., Ltd.
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
May 13, 2026Haaris Mehmood, Giorgos Tatsis, Dimitrios Alexopoulos +4Federated Learning Aggregation Communication-Efficient Distributed Training
Samsung R&D Institute UK (SRUK) · Information Technologies Institute (CERTH-ITI)
DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning
May 13, 2026Marc Molina Van den Bosch, Riccardo Taiello, Albert Sund Aillet +3Differentially Private Stochastic Gradient Descent Privacy-Preserving ML
CERN, Geneva, Switzerland · BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain · ICREA, Barcelona, Spain
LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections
May 13, 2026Mert Cihangiroglu, Alessandro Pegoraro, Phillip Rieger +2Communication-Efficient Distributed Training Privacy-Preserving ML
University of Pavia · 2Technical University of Darmstadt
AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
May 13, 2026Jacob Lagogiannis, William Agnew, Rosa I. Arriaga +1Adversarial Examples Privacy-Preserving ML
Franklin and Marshall College, United States of America · Carnegie Mellon University, United States of America · Georgia Institute of Technology, United States of America
GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization
May 12, 2026Huiran Duan, Qian Zhou, Zhongliang Guo +4Gait Analysis Privacy-Preserving ML
City University of New York, USA · Wuhan University, China · University of Aberdeen, UK +3
SoK: Unlearnability and Unlearning for Model Dememorization
May 12, 2026Mengying Zhang, Derui Wang, Ruoxi Sun +3Privacy-Preserving ML Machine Unlearning
RMIT University Australia · CSIRO Australia · University of Texas at Dallas USA +1
Revisiting Privacy Preservation in Brain-Computer Interfaces: Conceptual Boundaries, Risk Pathways, and a Protection-Strength Grading Framework
May 12, 2026Lei Sun, Xiuqing Mao, Shuai Zhang +4Brain-Computer Interfaces Privacy-Preserving ML
PLA Information Engineering University, Zhengzhou, 450001, Henan, China
Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable
May 11, 2026Tianyuan Zou, Liang Yue, Yang Liu +2Privacy-Preserving ML Video Surveillance
Institute for AI Industry Research, Tsinghua University, Beijing, China · RayNeo.AI, Shenzhen, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China
VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection
May 11, 2026Xiaobin Hu, Enpu Zuo, Lanping Hu +7Privacy Auditing Privacy-Preserving ML
National University of Singapore · Australian National University · The University of New South Wales +1
Deep Learning under Fractional-Order Differential Privacy
May 11, 2026Mohammad Partohaghighi, Roummel MarciaDifferentially Private Stochastic Gradient Descent Privacy-Preserving ML
MESA Lab, Dept. of EECS University of California, Merced Merced, CA 95343 · Dept. of Applied Mathematics University of California, Merced Merced, CA 95343

May 21, 2026, cs.CRJ/K move · Enter open · S save

Encrypted Neural Networks without Overflows

Philipp Kern, Lorenzo Rovida, Samuel Teuber, Edoardo Manino+2

Karlsruhe Institute of Technology, Germany · Polytechnic University of Turin, Italy · The University of Manchester, UK+2

Neural Network Verification Homomorphic Encryption Privacy-Preserving ML

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Fully homomorphic encryption (FHE) enables private inference by evaluating neural networks on encrypted data. In this way, we can delegate the computation to a third party server without ever revealing the user's data. Currently, the CKKS scheme is the backbone of most efficient FHE implementations, but it only supports addition, multiplication, and array rotation operations, thus requiring all activation functions of the neural network to be approximated by polynomials within a certain interval, imposing strict design tolerances. In this paper, we demonstrate for the first time that this scheme is vulnerable to overflow attacks, i.e., seemingly benign inputs that can exceed such tolerances of the FHE circuit, thereby causing corrupt and unusable outputs. To avoid them, we propose a formal verification technique that computes certified bounds on the ranges of all neurons in the network. By construction, our method eliminates overflows and, in our experiments, removed observed overflows on all benchmarks, reducing failure rates from up to 47% to 0%. Moreover, our overflow-free solution is compatible with most CKKS-based frameworks, as it allows to simply substitute standard polynomials by polynomials with rigorously designed ranges.

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