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

June 30, 2022

Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
Dongqi Fu, Jingrui He, Hanghang Tong, Ross Maciejewski
Code Generation Privacy Preserving Graph Learning Graph Data

June 24, 2022

zPROBE: Zero Peek Robustness Checks for Federated Learning
Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani, Xinqiao Zhang, Ke Huang, Farinaz Koushanfar
Federated Learning Privacy Preserving Byzantine Attack Zero Knowledge Robustness Verification Private Bayesian

June 21, 2022

WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning
Xueyang Wu, Shengqi Tan, Qian Xu, Qiang Yang
Federated Learning Ensemble Learning Privacy Preserving Heterogeneous Model Generic Plugin Federated Learning Platform

June 7, 2022

A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy
Yeqing Qiu, Chenyu Huang, Jianzong Wang, Zhangcheng Huang, Jing Xiao
Graph Neural Network Differential Privacy Privacy Preserving Subgraph Structure Federated Graph Neural Network

May 28, 2022

Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks
Xiao Han, Leye Wang, Junjie Wu, Yuncong Yang
Network Programming Privacy Preserving Attribute Inference Attack Privacy Advantage

May 27, 2022

May 26, 2022

Cali3F: Calibrated Fast Fair Federated Recommendation System
Zhitao Zhu, Shijing Si, Jianzong Wang, Jing Xiao
Privacy Preserving Recommendation Performance Federated Recommendation Recommendation Fairness

May 24, 2022

A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition
Yanling Hao, Zhiyuan Shi, Yuanwei Liu
Activity Recognition Privacy Preserving Based Deep Learning Wireless Vision

May 23, 2022

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
Ljubomir Rokvic, Panayiotis Danassis, Sai Praneeth Karimireddy, Boi Faltings
Privacy Preserving Data Quality Lazy Search Influence Estimation

May 13, 2022

On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning
Wenxuan Bao, Luke A. Bauer, Vincent Bindschaedler
Differential Privacy Feature Selection Privacy Preserving Importance Aware Architecture Design Private Machine Learning Private Learning Algorithm

April 29, 2022

Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval
Shupeng Su, Binjie Zhang, Yixiao Ge, Xuyuan Xu, Yexin Wang, Chun Yuan, Ying Shan
Privacy Preserving Image Retrieval Bidirectional Training Backward Compatible Training Model Upgrade Forward Compatible Training

April 22, 2022

Towards Privacy-Preserving Neural Architecture Search
Fuyi Wang, Leo Yu Zhang, Lei Pan, Shengshan Hu, Robin Doss
Deep Learning Model Neural Architecture Search Privacy Preserving Secure Multi Party Computation SEMI SuperYOLO Na

April 21, 2022

FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie
Contrastive Learning Privacy Preserving Effective Recommendation Recommendation Model Local Differential Privacy Federated Contrastive Learning

April 20, 2022

You Are What You Write: Preserving Privacy in the Era of Large Language Models
Richard Plant, Valerio Giuffrida, Dimitra Gkatzia
Large Language Model Differential Privacy Privacy Preserving New Era Privacy Enhancing Technology

April 19, 2022

Invertible Mask Network for Face Privacy-Preserving
Yang Yang, Yiyang Huang, Ming Shi, Kejiang Chen, Weiming Zhang, Nenghai Yu
Privacy Preserving Human Face Mask Frozen DETR

April 5, 2022

Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption
Haleh Hayati, Carlos Murguia, Nathan van de Wouw
Privacy Preserving Privacy Preserving Federated Learning Immersive Environment Matrix Encryption

April 2, 2022

Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning
Héber H. Arcolezi
Machine Learning Financial Application Differential Privacy Privacy Preserving Production Incident Private Machine Learning Privacy Utility Trade Novel Concept

March 31, 2022

Rethinking Portrait Matting with Privacy Preserving
Sihan Ma, Jizhizi Li, Jing Zhang, He Zhang, Dacheng Tao
Privacy Preserving Face Image Image Matting

March 30, 2022

Decentralized Collaborative Learning Framework for Next POI Recommendation
Jing Long, Tong Chen, Nguyen Quoc Viet Hung, Hongzhi Yin
Differential Privacy Privacy Preserving Decentralized Learning Point of Interest Location Based Social Network