BLAZE: Blazing Fast Privacy-Preserving Machine Learning

18 May 2020

Ajith Suresh

Papers citing "BLAZE: Blazing Fast Privacy-Preserving Machine Learning"

22 / 22 papers shown

Title
Flash: A Hybrid Private Inference Protocol for Deep CNNs with High Accuracy and Low Latency on CPU H. Roh Jinsu Yeo Yeongil Ko Gu-Yeon Wei David Brooks Woo-Seok Choi 89 2 0 20 Jan 2025
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey Chaoyu Zhang Shaoyu Li AILaw 66 3 0 25 Feb 2024
CompactTag: Minimizing Computation Overheads in Actively-Secure MPC for Deep Neural Networks Yongqin Wang Pratik Sarkar Nishat Koti A. Patra Murali Annavaram 27 2 0 08 Nov 2023
Bicoptor 2.0: Addressing Challenges in Probabilistic Truncation for Enhanced Privacy-Preserving Machine Learning Lijing Zhou Qingrui Song Su Zhang Ziyu Wang Xianggui Wang Yong-Lu Li 11 4 0 10 Sep 2023
ExTRUST: Reducing Exploit Stockpiles with a Privacy-Preserving Depletion System for Inter-State Relationships Thomas Reinhold Philip D. . Kuehn Daniel Gunther T. Schneider Christian A. Reuter 16 1 0 01 Jun 2023
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy Yifei Zhang Dun Zeng Jinglong Luo Zenglin Xu Irwin King FedML 84 48 0 21 Feb 2023
Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy Yusuke Kawamoto Kazumasa Miyake K. Konishi Y. Oiwa 29 4 0 18 Jan 2023
Hercules: Boosting the Performance of Privacy-preserving Federated Learning Guowen Xu Xingshuo Han Shengmin Xu Tianwei Zhang Hongwei Li Xinyi Huang R. Deng FedML 35 16 0 11 Jul 2022
MPClan: Protocol Suite for Privacy-Conscious Computations Nishat Koti S. Patil A. Patra Ajith Suresh 24 18 0 24 Jun 2022
Towards Practical Privacy-Preserving Solution for Outsourced Neural Network Inference Pinglan Liu Wensheng Zhang FedML 19 3 0 06 Jun 2022
Privacy-Preserving Epidemiological Modeling on Mobile Graphs Daniel Gunther Marco Holz B. Judkewitz Helen Mollering Benny Pinkas T. Schneider Ajith Suresh 34 4 0 01 Jun 2022
SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation Yash More Prashanthi Ramachandran Priyam Panda A. Mondal Harpreet Virk Debayan Gupta FedML 27 11 0 19 Jan 2022
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale Karthik Garimella N. Jha Zahra Ghodsi S. Garg Brandon Reagen 33 3 0 04 Nov 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 40 16 0 20 Sep 2021
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 57 184 0 22 Apr 2021
Multi-party computation enables secure polynomial control based solely on secret-sharing Sebastian Schlor Michael Hertneck Stefan Wildhagen Frank Allgöwer 30 9 0 30 Mar 2021
Secrecy: Secure collaborative analytics on secret-shared data J. Liagouris Vasiliki Kalavri Muhammad Faisal Mayank Varia 21 19 0 01 Feb 2021
Confidential Machine Learning on Untrusted Platforms: A Survey Sagar Sharma Keke Chen FedML 22 15 0 15 Dec 2020
POSEIDON: Privacy-Preserving Federated Neural Network Learning Sinem Sav Apostolos Pyrgelis J. Troncoso-Pastoriza D. Froelicher Jean-Philippe Bossuat João Sá Sousa Jean-Pierre Hubaux FedML 11 153 0 01 Sep 2020
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing T. Ryffel Pierre Tholoniat D. Pointcheval Francis R. Bach FedML 28 94 0 08 Jun 2020
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning Nishat Koti Mahak Pancholi A. Patra Ajith Suresh 14 138 0 20 May 2020
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning Sameer Wagh Shruti Tople Fabrice Benhamouda E. Kushilevitz Prateek Mittal T. Rabin FedML 33 295 0 05 Apr 2020