A Quantitative Metric for Privacy Leakage in Federated Learning

24 February 2021

Papers citing "A Quantitative Metric for Privacy Leakage in Federated Learning"

9 / 9 papers shown

Title
Shielding Latent Face Representations From Privacy Attacks Arjun Ramesh Kaushik Bharat Chandra Yalavarthi Arun Ross Vishnu Boddeti Nalini Ratha 17 0 0 19 May 2025
Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption Bharat Chandra Yalavarthi Arjun Ramesh Kaushik Arun Ross Vishnu Boddeti Nalini Ratha PICV 32 2 0 24 Apr 2024
Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting Asif Iqbal P. Gope Biplab Sikdar 39 2 0 03 Mar 2024
FederatedTrust: A Solution for Trustworthy Federated Learning Pedro Miguel Sánchez Sánchez Alberto Huertas Celdrán Ning Xie Gérome Bovet Gregorio Martínez Pérez Burkhard Stiller 38 21 0 20 Feb 2023
A Survey on Gradient Inversion: Attacks, Defenses and Future Directions Rui Zhang Song Guo Junxiao Wang Xin Xie Dacheng Tao 35 36 0 15 Jun 2022
Gradient Obfuscation Gives a False Sense of Security in Federated Learning Kai Yue Richeng Jin Chau-Wai Wong D. Baron H. Dai FedML 36 46 0 08 Jun 2022
Subject Membership Inference Attacks in Federated Learning Anshuman Suri Pallika H. Kanani Virendra J. Marathe Daniel W. Peterson 30 25 0 07 Jun 2022
Cali3F: Calibrated Fast Fair Federated Recommendation System Zhitao Zhu Shijing Si Jianzong Wang Jing Xiao FedML 81 14 0 26 May 2022
Gradient Leakage Attack Resilient Deep Learning Wenqi Wei Ling Liu SILM PILM AAML 29 47 0 25 Dec 2021