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

October 4, 2022

OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Xiaochen Li, Yuke Hu, Weiran Liu, Hanwen Feng, Li Peng, Yuan Hong, Kui Ren, Zhan Qin
Differential Privacy Privacy Preserving Vertical Federated Learning Local Differential Privacy Image Desensitization Federated Gradient Boosting

September 29, 2022

PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems
Peihua Mai, Yan Pang
Recommender System Privacy Preserving Matrix Factorization Homomorphic Encryption Federated Recommendation

September 28, 2022

September 23, 2022

Differentially private partitioned variational inference
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
Differential Privacy Variational Inference Privacy Preserving Differential Privacy Guarantee

September 20, 2022

Audit and Improve Robustness of Private Neural Networks on Encrypted Data
Jiaqi Xue, Lei Xu, Lin Chen, Weidong Shi, Kaidi Xu, Qian Lou
Neural Network Native Robustness Privacy Preserving Audit Evidence Robust SNN

September 18, 2022

Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big data
Leijie Zhang, Ye Shi, Yu-Cheng Chang, Chin-Teng Lin
Neural Network Privacy Preserving Sensitive Data Big Data Privacy Preservation Heterogeneous Datasets

September 10, 2022

Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation
Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje
Privacy Preserving PRIvacy Leakage Privacy Guarantee Privacy Enhancing Technology Cross Domain Knowledge Distillation

September 8, 2022

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
Yan Kang, Jiahuan Luo, Yuanqin He, Xiaojin Zhang, Lixin Fan, Qiang Yang
New Framework Privacy Preserving Vertical Federated Learning Privacy Attack Privacy Utility Privacy Utility Trade Label Inference Attack

September 7, 2022

On the utility and protection of optimization with differential privacy and classic regularization techniques
Eugenio Lomurno, Matteo matteucci
Differential Privacy Optimization Purpose Privacy Preserving Task Utility Model Inversion Attack Private Stochastic Regularization Method

September 3, 2022

Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning
Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das
Data Driven Early Stage Convergence Privacy Preserving Federated Learning Approach Building Dataset Noise Suppression Intelligent Sensing Communication Round

August 27, 2022

RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems
Emna Baccour, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani
Deep Neural Network Privacy Preserving Low Latency Deep Learning Inference Privacy Aware Collaborative Deep Neural Network Inference

August 26, 2022

Another Use of SMOTE for Interpretable Data Collaboration Analysis
Akira Imakura, Masateru Kihira, Yukihiko Okada, Tetsuya Sakurai
Privacy Preserving Greater Public Use SMOTE Integrated Data Collaboration

August 22, 2022

DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting
Timour Igamberdiev, Thomas Arnold, Ivan Habernal
Differential Privacy Privacy Preserving Transparency Index Research Reproducibility PRIvacy Leakage Private Text

August 19, 2022

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
Zachary Charles, Kallista Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Agüera y Arcas
Federated Learning Privacy Preserving Timely Communication Client Selection Negative Primitive

August 15, 2022

Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks
Mengyuan Lee, Guanding Yu, Huaiyu Dai
Graph Neural Network Privacy Preserving Wireless Network Privacy Utility Privacy Preserving Inference Decentralized Inference

August 13, 2022

Practical Vertical Federated Learning with Unsupervised Representation Learning
Zhaomin Wu, Qinbin Li, Bingsheng He
Privacy Preserving Vertical Federated Learning Unsupervised Representation Learning

August 4, 2022

Privacy-Preserving Action Recognition via Motion Difference Quantization
Sudhakar Kumawat, Hajime Nagahara
Action Recognition Privacy Preserving Private Training

July 21, 2022

Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition
Han Sun, Yu Chen
Action Recognition Privacy Preserving Human SAFETY Wearable Sensor

July 4, 2022

Federated Split GANs
Pranvera Kortoçi, Yilei Liang, Pengyuan Zhou, Lik-Hang Lee, Abbas Mehrabi, Pan Hui, Sasu Tarkoma, Jon Crowcroft
Generative Adversarial Network GAN Model Privacy Preserving Generative Adversarial GAN Based

Privacy Preserving

Papers

OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization

PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems

Securing Federated Learning against Overwhelming Collusive Attackers

Momentum Gradient Descent Federated Learning with Local Differential Privacy

Differentially private partitioned variational inference

Audit and Improve Robustness of Private Neural Networks on Encrypted Data

Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big data

Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning

On the utility and protection of optimization with differential privacy and classic regularization techniques

Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning

RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems

Another Use of SMOTE for Interpretable Data Collaboration Analysis

DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning

Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks

Practical Vertical Federated Learning with Unsupervised Representation Learning

Privacy-Preserving Action Recognition via Motion Difference Quantization

Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition

Federated Split GANs