Privacy Preserving

Privacy-preserving techniques aim to enable data analysis and machine learning while safeguarding sensitive information. Current research focuses on developing and improving methods like differential privacy, federated learning, homomorphic encryption, and data obfuscation, often applied to specific model architectures such as transformers and neural radiance fields. These advancements are crucial for addressing privacy concerns in various applications, including healthcare, finance, and AI-powered services, allowing for collaborative data analysis and model training without compromising individual privacy. The field is actively exploring the trade-offs between privacy guarantees, model accuracy, and computational efficiency.

Papers

February 3, 2023

Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis
Differential Privacy Medical Imaging Privacy Preserving Large Scale Training Privacy Preserving Training Fairness Estimation

February 2, 2023

Federated Analytics: A survey
Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han, Shantanu Sharma, Chaoyang He, Sharad Mehrotra, Salman Avestimehr
Timely Survey Privacy Preserving Data Analytics Federated Data

January 30, 2023

FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang, Lixin Fan, Qiang Yang
Privacy Preserving Deep Learning Framework Vertical Federated Learning Obfuscation Technique Private Feature

January 27, 2023

FedPH: Privacy-enhanced Heterogeneous Federated Learning
Kuang Hangdong, Mi Bo
Differential Privacy Federated Learning Privacy Preserving Heterogeneous Federated Learning

January 23, 2023

Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer
Teru Nagamori, Hitoshi Kiya
Deep Learning Vision Transformer Image Classification Privacy Preserving Privacy Preserving Image Cifar 10 Image Encryption

January 16, 2023

Dual Personalization on Federated Recommendation
Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang, Chengqi Zhang, Bo Yang
Privacy Preserving Federated Recommendation Dual Domain Anti Personalization

January 12, 2023

Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks
Zheng Qi, AprilPyone MaungMaung, Hitoshi Kiya
Deep Learning Privacy Preserving Random Neural Network Private Neural

January 8, 2023

STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition
Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, Shuicheng Yan
Action Recognition Privacy Preserving Random Sparsification

January 3, 2023

Recent Advances on Federated Learning: A Systematic Survey
Bingyan Liu, Nuoyan Lv, Yuanchun Guo, Yawen Li
LeArning Abstract Comprehensive Survey Privacy Preserving Recent Advance Learning Method

January 2, 2023

Ranking Differential Privacy
Shirong Xu, Will Wei Sun, Guang Cheng
Differential Privacy Privacy Preserving Partial Ranking Optimal Ranking

December 16, 2022

Mobile Augmented Reality with Federated Learning in the Metaverse
Xinyu Zhou, Jun Zhao
Raw Data Privacy Preserving Augmented Reality Ubiquitous Semantic Metaverse Federated Meta Learning

December 1, 2022

Differentially Private Learning with Per-Sample Adaptive Clipping
Tianyu Xia, Shuheng Shen, Su Yao, Xinyi Fu, Ke Xu, Xiaolong Xu, Xing Fu
Differential Privacy Privacy Preserving Private Learning Adaptive Clipping Gradient Descent Batch Size

November 24, 2022

A Privacy-Preserving Outsourced Data Model in Cloud Environment
Rishabh Gupta, Ashutosh Kumar Singh
Differential Privacy Privacy Preserving Cloud Computing Cloud Environment

November 19, 2022

A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors
Xiaoshan Zhou, Pin-Chao Liao
Deep Learning Privacy Preserving Internet of Thing Device EEG Data Brain Signal Electrophysiological Data

November 13, 2022

Towards Privacy-Aware Causal Structure Learning in Federated Setting
Jianli Huang, Xianjie Guo, Kui Yu, Fuyuan Cao, Jiye Liang
Federated Learning Privacy Preserving Causal Structure Federated Setting Causal Structure Learning

November 7, 2022

HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse
Shenglai Zeng, Zonghang Li, Hongfang Yu, Zhihao Zhang, Long Luo, Bo Li, Dusit Niyato
Privacy Preserving Heterogeneous Federated Learning Ubiquitous Semantic Metaverse Federated Averaging Edge Intelligence

November 2, 2022

Privacy-preserving Non-negative Matrix Factorization with Outliers
Swapnil Saha, Hafiz Imtiaz
Privacy Preserving Matrix Factorization Private Algorithm Non Negative Matrix Factorization

October 21, 2022

Privacy-Preserved Neural Graph Similarity Learning
Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen
Privacy Preserving Graph Matching Graph Similarity Learning

October 20, 2022

Learning-Augmented Private Algorithms for Multiple Quantile Release
Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii
Differential Privacy Privacy Preserving Sensitive Data Quantile Regression Private Algorithm Learning Augmented Algorithm

October 12, 2022

Privacy of federated QR decomposition using additive secure multiparty computation
Anne Hartebrodt, Richard Röttger
Privacy Preserving Privacy Policy Secure Multiparty Computation QR Decomposition