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

May 9, 2023

May 6, 2023

Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, G. Edward Suh
Privacy Preserving General Bound Fisher Information Privacy Sensitive Encoding Scheme Restricted Approximate Invertibility

April 17, 2023

Dynamics-Based Algorithm-Level Privacy Preservation for Push-Sum Average Consensus
Huqiang Cheng, Xiaofeng Liao, Huaqing Li, Qingguo Lü
Differential Privacy Privacy Preserving Average Consensus

April 11, 2023

Privacy-Preserving Matrix Factorization for Recommendation Systems using Gaussian Mechanism
Sohan Salahuddin Mugdho, Hafiz Imtiaz
Differential Privacy Privacy Preserving Matrix Factorization Recommendation System Gaussian Mechanism

April 7, 2023

Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression
John Chiang
Convolutional Neural Network Transfer Learning Privacy Preserving Homomorphic Encryption Homomorphic Logistic Regression Training

March 29, 2023

The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems
Adam K. Taras, Niko Suenderhauf, Peter Corke, Donald G. Dansereau
Robotics Domain Vision Paper Privacy Preserving Community Need Robot Capability Robot Vision Robot Gaze Trustworthy Autonomous System

March 17, 2023

Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs
Masakazu Ohno, Riki Ukyo, Tatsuya Amano, Hamada Rizk, Hirozumi Yamaguchi
Privacy Preserving High Precision Lidar 3D LiDAR Pedestrian Tracking Pedestrian Recognition

March 8, 2023

Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles
Muzi Peng, Jiangwei Wang, Dongjin Song, Fei Miao, Lili Su
Autonomous Vehicle Trajectory Prediction Privacy Preserving Objective Function Connected Autonomous Vehicle

March 7, 2023

A Privacy Preserving System for Movie Recommendations Using Federated Learning
David Neumann, Andreas Lutz, Karsten Müller, Wojciech Samek
Recommender System Privacy Preserving Movie Recommendation Data Privacy Personal Information

March 3, 2023

Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens
Ayoub Arous, Amira Guesmi, Muhammad Abdullah Hanif, Ihsen Alouani, Muhammad Shafique
Privacy Preserving Privacy Guarantee Hyper Parameter Private Stochastic Gradient Descent Private Model Learning Privacy Utility Tradeoff

March 1, 2023

FedScore: A privacy-preserving framework for federated scoring system development
Siqi Li, Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Chuan Hong, Feng Xie, Han Yuan, Mingxuan Liu, Daniel M. Buckland, Yong Chen, Nan Liu
Privacy Preserving Scoring Method

February 28, 2023

mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor
Kevin Mitchell, Khaled Kassem, Chaitanya Kaul, Valentin Kapitany, Philip Binner, Andrew Ramsay, Roderick Murray-Smith, Daniele Faccio
Privacy Preserving Radar Data Radar Sensor Millimeter Wave Radar Surveillance System

February 26, 2023

P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups
Ioannis Arapakis, Panagiotis Papadopoulos, Kleomenis Katevas, Diego Perino
Differential Privacy Privacy Preserving Privacy Guarantee Peer to Peer Peer Learning Cryptographic Primitive

February 10, 2023

February 9, 2023

Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading
Shuying Gan, Marie Siew, Chao Xu, Tony Q. S. Quek
Privacy Preserving Mobile Edge Computing Task Offloading Private Reinforcement Learning

February 7, 2023

A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
Haobo Zhang, Junyuan Hong, Fan Dong, Steve Drew, Liangjie Xue, Jiayu Zhou
Federated Learning Privacy Preserving Credit Card Fraud Financial Fraud Detection Payment Channel

February 5, 2023

RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo, Nuo Xu, Shijin Duan, Ran Ran, Jiahui Zhao, Shaoyi Huang, Xi Xie, Chenghong Wang, Tong Geng, Wujie Wen, Xiaolin Xu, Caiwen Ding
Neural Network Privacy Preserving Field Programmable Gate Array Multi Party Party Computation Cryptographic Building Block

Privacy Preserving

Papers

Collaborative Chinese Text Recognition with Personalized Federated Learning

Turning Privacy-preserving Mechanisms against Federated Learning

Privacy in Speech Technology

Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information

Dynamics-Based Algorithm-Level Privacy Preservation for Push-Sum Average Consensus

Privacy-Preserving Matrix Factorization for Recommendation Systems using Gaussian Mechanism

Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression

The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems

Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs

Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles

A Privacy Preserving System for Movie Recommendations Using Federated Learning

Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens

FedScore: A privacy-preserving framework for federated scoring system development

mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor

P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups

Privacy Against Agnostic Inference Attacks in Vertical Federated Learning

On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence

Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading

A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection

RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference