Privacy-Preserving Representation Learning

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11 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

PrinGS: Privacy Preservation in Gaussian Splatting
Oct 26, 2026Gaurav Behera, Abhinav Raundhal, Archisha Panda +33D Gaussian Splatting Privacy-Preserving Representation Learning
IIIT Hyderabad, India
Principled Nonlinear Concept Erasure and Steering
Oct 23, 2026Idan Simai, Shauli Ravfogel, Uri ShahamConcept Erasure Privacy-Preserving Representation Learning
Department of Computer Science, Bar-Ilan University · Center for Data Science, New York University
FedPix: Learning What to Share with Less Leakage in Federated Intelligence
Oct 15, 2026Jingwei Zhao, Tiantong Sun, Yuanyuan Song +6Privacy-Preserving Representation Learning Privacy-Preserving ML
Huazhong University of Science and Technology, Wuhan, China · George Mason University, Fairfax, VA, USA
Privacy-Preserving Deep Joint Source-Channel Coding with In-Loop Concept Erasure
Sep 11, 2026Rami Eid, Maria Slim, Mariette Awad +1Deep Joint Source-Channel Coding Privacy-Preserving ML
Department of Electrical and Computer Engineering, American University of Beirut, Beirut 1107 2020, Lebanon
Chemical and geometric representation fidelity improves drug--target affinity prediction
Sep 2, 2026Yixiao Li, Yining Qian, Yefan Chen +5Privacy-Preserving Representation Learning Protein-Ligand Binding Affinity Prediction
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China · School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China +1
TrustCLIP: Learning Private Visual Features via Adversarial Reconstruction
Jul 5, 2026Nikos Athanasiou, Ilya A. Petrov, Angela Yao +7Gradient Inversion Attacks VLM Robustness
Meta, Zürich, Switzerland · Max Planck Institute for Intelligent Systems, Tübingen, Germany · University of Tübingen, Germany +1
Distributed Deep Variational Approach for Privacy-preserving Data Release
May 4, 2026Zahir Alsulaimawi, Huaping LiuInformation Bottleneck Privacy-Preserving ML
Variational Feature Compression for Model-Specific Representations
Apr 8, 2026Zinan Guo, Zihan Wang, Chuan Yan +3Privacy-Preserving Representation Learning Privacy-Preserving ML
The University of Queensland, Brisbane, Australia · University of Wollongong, Wollongong, Australia · City University of Hong Kong, Hong Kong SAR, China
Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning
Mar 16, 2026Jeremy J SamuelsonPrivacy-Preserving Representation Learning Privacy-Preserving ML
EVP, Artificial Intelligence & Innovation Integrated Quantum Technologies
Bodhi VLM: Privacy-Alignment Modeling for Hierarchical Visual Representations in Vision Backbones and VLM Encoders via Bottom-Up and Top-Down Feature Search
Mar 14, 2026Bo Ma, Wei Qi Yan, Jinsong WuPrivacy-Preserving Representation Learning Privacy-Preserving ML
Auckland University of Technology, Auckland 1024 NZ · Resideo Technologies, Inc., Austin, TX 78759, USA · Guilin University of Electronic Technology, Gui Lin, China +1
CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement
Mar 12, 2026Alex Gn, Fan Li, S Kuniyilh +1Privacy-Preserving Representation Learning Human Activity Recognition

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PrinGS: Privacy Preservation in Gaussian Splatting

Gaurav Behera, Abhinav Raundhal, Archisha Panda, Saumya Balina+2

IIIT Hyderabad, India

3D Gaussian Splatting Privacy-Preserving Representation Learning 3DGS Training

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3D Gaussian Splatting (3DGS) has rapidly become the preferred representation for high-fidelity novel view synthesis and 3D scene reconstruction. However, the learned representation encodes sensitive visual information including human faces, license plates, documents, and computer screens, raising significant privacy concerns when trained splats are shared or deployed. In this work, we address the underexplored problem of privacy preservation in Gaussian Splatting by proposing a representation-level anonymization framework that directly anonymizes a trained splat. Given a trained splat and a user-provided reference view identifying sensitive content, our method localizes the corresponding regions in the underlying 3D representation and directly anonymizes them, ensuring that the sensitive information is absent from all subsequently rendered novel views while preserving the overall scene geometry and visual fidelity. We achieve this post-training anonymization in less than 1 second. Furthermore, we extend our framework with a privacy-by-design training strategy that prevents sensitive content from being embedded into the representation during optimization, enabling the generation of privacy-preserving splats directly from training images. Our method achieves a remarkably low Face Similarity Measure (FSM) of 9.79% at a retained Face Detection Rate (FDR) of 39.47% and makes sensitive text completely unreadable with an OCR Character Error Rate of 1.00. Our work aims to serve as the primary stepping stone towards privacy-aware 3D representations and motivates further research along these directions.