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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Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption
Jun 3, 2026Jian Yang, Yuan Tong, Qinbin Li +2Causal Discovery Causal Structure Learning
Hong Kong University of Science and Technology (Guangzhou) · Hong Kong University of Science and Technology · University of California, Berkeley
DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
Jun 3, 2026Yunsheng Yuan, Xue Xiao, Lina Wang +1Decentralized Learning Privacy-Preserving ML
School of Computer Science and Technology, Shandong University, Qingdao 266237, China · Inspur Cloud Information Technology Co, Ltd.
Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving
Jun 3, 2026Xixi Tian, Di Wu, Xiang Liu +4Privacy-Preserving ML Clinical Prediction
College of Computer and Information Science Southwest University Chongqing, China · Department of Anesthesiology Southwest Hospital Chongqing, China
PURGE: Projected Unlearning via Retain-Guided Erasure
Jun 2, 2026Vedant Jawandhia, Daksh Ahuja, Ghufran Alam Siddiqui +3Privacy-Preserving ML Gradient Projection
1BITS Pilani, Pilani Campus, India
Bayesian Membership Privacy for Graph Neural Networks
Jun 2, 2026Sinan Yıldırım, Megha KhoslaPrivacy Auditing Graph Neural Networks
Faculty of Engineering and Natural Sciences Sabancı University Istanbul, Turkey · Intelligent Systems Department Delft University of Technology (TU Delft) Delft, The Netherlands
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Jun 1, 2026Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella SunFederated Learning Aggregation Privacy-Preserving ML
University of Tennessee, Knoxville, USA · Oak Ridge National Laboratory, USA
Randomized Least Squares Value Iteration itself is Joint Differentially Private
Jun 1, 2026Haiyang Lu, Pratik Gajane, Shaojie Bai +1Markov Decision Processes RL Exploration
1Laboratoire d’Informatique Fondamentale d’Orléans (LIFO), Université d’Orléans · College of Control Science and Engineering, Zhejiang University · Department of Computer Science, University of Copenhagen
Private and Stable Test-Time Adaptation with Differential Privacy
Jun 1, 2026Zefeng Li, Qiaoyue Tang, Mathias Lecuyer +1Continual Test-Time Adaptation Privacy-Preserving ML
University of British Columbia · Vector Institute
Fair Finetuning Mitigates Distribution Inference Attacks
Jun 1, 2026Rakshit NaiduData Leakage Algorithmic Fairness
Georgia Institute of Technology
Differentially Private Datastore Generation for Retrieval-Augmented Inference
May 31, 2026Abdelrahman Abouelenein, Marwan TorkiPrivacy-Preserving ML Differential Privacy
Department of Computer and Systems Engineering Alexandria University, Egypt · Microsoft
Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
May 31, 2026Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi +1Gradient Inversion Attacks Adversarial Attacks
Department of Computing, Jönköping University, Jönköping, Sweden · Scaleout Systems, Uppsala, Sweden · Department of Information Systems, University of Haifa, Haifa, Israel
Silent Failures in Federated Personalization of Foundation Models
May 31, 2026YongKyung Oh, Alex BuiAlgorithmic Fairness Privacy-Preserving ML
Medical & Imaging Informatics (MII) Group, University of California, Los Angeles (UCLA) Los Angeles, CA, 90024, USA
PRISM: Gauge-Invariant Tangent-Space Differentially Private LoRA
May 31, 2026Shihao Wang, Xueru ZhangDifferentially Private Stochastic Gradient Descent Low-Rank Adaptation
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
Multi-Agent Conformal Prediction with Personalized Statistical Validity
May 30, 2026Martin V. Vejling, Christophe A. N. Biscio, Adrien Mazoyer +2Weighted Conformal Prediction Conformal Prediction
Department of Electronic Systems Aalborg University, Aalborg, Denmark · Department of Mathematical Sciences Aalborg University, Aalborg, Denmark · Institut de Mathématiques de Toulouse Université de Toulouse, Toulouse, France
Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings
May 29, 2026Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj SharmaFederated Continual Learning Replay-Based Continual Learning
Department of Computer Science, Iowa State University · Department of Computer Science & Engineering, BRAC University · Department of Civil, Construction & Environmental Engineering, Iowa State University
PE-means: Improved Differentially Private $k$ -means Clustering through Private Evolution
May 29, 2026Thomas Humphries, Zinan Lin, Sergey YekhaninClustering Privacy-Preserving ML
University of Waterloo · Microsoft Research
DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs
May 29, 2026Ashley Hoi-Ting Au, Zikun Zhang, Ligang He +1Communication-Efficient Distributed Training Federated Graph Learning
Department of Computer Science, The University of Warwick · School of Computing and Communications, Lancaster University
Differentially Private Preference Data Synthesis for Large Language Model Alignment
May 29, 2026Fengyu Gao, Jing YangPairwise Preference Learning LLM Alignment
Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA · Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia, USA.
MosaicLeaks:Privacy Risks in Querying-in-the-Open for Deep Research Agents
May 29, 2026Alexander Gurung, Spandana Gella, Alexandre Drouin +3Data Leakage Deep Research Agents
University of Edinburgh · ServiceNow AI Research · McGill University +2
Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels
May 28, 2026Anthony Ayli, Khalil Harris, Jihad Fahs +1Federated Learning Aggregation Homomorphic Encryption
FPLIER: Federated Pathway-Level Information Extractor
May 28, 2026Daniele Malpetti, Christian Berchtold, Francesco Gualdi +3Membership Inference Attacks Privacy-Preserving ML
1Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI, Lugano, Switzerland · 2Swiss Institute of Bioinformatics (SIB), Lugano, Switzerland
Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
May 27, 2026Anderson AugusmaMultimodal Emotion Recognition Privacy-Preserving ML
Thèse de Doctorat de l’Université Grenoble Alpes
Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
May 27, 2026Yvonne Zhou, Mingyu Liang, Ivan Brugere +5Differentially Private Stochastic Gradient Descent Homomorphic Encryption
University of Maryland, College Park, MD 20742 · J.P. Morgan AI Research, New York, NY, 10017 · AlgoCRYPT CoE
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
May 26, 2026Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1Privacy-Preserving ML Kernel Methods
Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan · Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Practical Anonymous Two-Party Gradient Boosting Decision Tree
May 26, 2026Chenyu Huang, Fan Zhang, Minxin Du +6Secure Multi-Party Computation Gradient-Boosted Decision Trees
Tencent · Hong Kong Polytechnic University · Chinese University of Hong Kong +1
Closed-Form Node Classification with Exact Graph Unlearning
May 25, 2026Aditya Gaur, Charu SharmaGraph Representation Learning Privacy-Preserving ML
Machine Learning Lab · IIIT Hyderabad
CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering
May 23, 2026Matilda Gaddi, Jin Noh, Onat Gungor +1Cybersecurity LLMs for Cybersecurity
Department of Computer Science and Engineering University of California, San Diego (UCSD)
Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems
May 23, 2026Imesh Ekanayake, Elham Naghizade, Jeffrey ChanAdversarial Training Representation Learning
School of Computing Technologies, RMIT University, Melbourne, VIC, Australia
PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
May 22, 2026Anisa Halimi, Liubov Nedoshivina, Kieran Fraser +1Healthcare Privacy-Preserving ML
IBM Research Dublin, Ireland
Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
May 22, 2026Alexander Long, Chamin Hewa Koneputugodage, Thalaiyasingam Ajanthan +5LLM Inference Decentralized Learning
Pluralis Research

Jun 3, 2026, cs.CRJ/K move · Enter open · S save

Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption

Jian Yang, Yuan Tong, Qinbin Li, Zeyi Wen+1

Hong Kong University of Science and Technology (Guangzhou) · Hong Kong University of Science and Technology · University of California, Berkeley

Causal Discovery Causal Structure Learning Homomorphic Encryption Privacy-Preserving ML

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Preserving data privacy is an important topic in structural data management and data mining. However, the issue of privacy leakage in distributed causal structure learning is a persistent challenge, especially in cases where data transmission and computation are required. In this paper, we propose a method based on fully homomorphic encryption (FHE) that performs calculations on ciphertexts, keeping data encrypted in transition and computation. Nevertheless, adopting FHE to causal structure learning is challenging due to the high computation cost and limited support on division as well as logarithm operations in FHE. To tackle this challenge, we propose a series of novel techniques including (i) circuit simplification for better efficiency, (ii) approximation of division and logarithm through Newton-Raphson Reciprocal and Taylor expansion, and (iii) a batching technique with SIMD-acceleration to enhance the whole learning process. Additionally, our method can be easily extended beyond FHE by demonstration of its portability to support differential privacy. Empirical results show that our method achieves high consistency and comparable causal structure with the plaintext version in the datasets tested. Last, our method is efficient and practical to complete learning causal structures in tens of minutes even under the privacy protection of FHE.

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