FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning

5 April 2020

Papers citing "FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning"

45 / 95 papers shown

Title
A Survey of Face Recognition Xinyi Wang Jianteng Peng Sufang Zhang Bihui Chen Yi Wang Yan-Hua Guo CVBM 23 0 0 26 Dec 2022
Private Multiparty Perception for Navigation Hui Lu Mia Chiquier Carl Vondrick EgoV 33 0 0 02 Dec 2022
Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware Pengzhi Huang Thang Hoang Yueying Li Elaine Shi G. E. Suh 22 2 0 18 Oct 2022
Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning Lijing Zhou Ziyu Wang Hongrui Cui Qingrui Song Yu Yu 41 11 0 05 Oct 2022
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning Tanguy Marchand Boris Muzellec C. Béguier Jean Ogier du Terrail M. Andreux FedML 36 9 0 04 Oct 2022
MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference Yongqin Wang Rachit Rajat Murali Annavaram 14 2 0 27 Sep 2022
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy Wenqiang Ruan Ming Xu Wenjing Fang Li Wang Lei Wang Wei Han 32 12 0 18 Aug 2022
Privacy-Preserving Federated Recurrent Neural Networks Sinem Sav Abdulrahman Diaa Apostolos Pyrgelis Jean-Philippe Bossuat Jean-Pierre Hubaux 6 7 0 28 Jul 2022
Characterizing and Optimizing End-to-End Systems for Private Inference Karthik Garimella Zahra Ghodsi N. Jha S. Garg Brandon Reagen 39 25 0 14 Jul 2022
MPClan: Protocol Suite for Privacy-Conscious Computations Nishat Koti S. Patil A. Patra Ajith Suresh 19 18 0 24 Jun 2022
FLVoogd: Robust And Privacy Preserving Federated Learning Yuhang Tian Rui Wang Yan Qiao E. Panaousis K. Liang FedML 26 4 0 24 Jun 2022
Towards End-to-End Private Automatic Speaker Recognition Francisco Teixeira A. Abad Bhiksha Raj Isabel Trancoso 35 10 0 23 Jun 2022
Towards Practical Privacy-Preserving Solution for Outsourced Neural Network Inference Pinglan Liu Wensheng Zhang FedML 14 3 0 06 Jun 2022
CryptoTL: Private, Efficient and Secure Transfer Learning Roman Walch Samuel Sousa Lukas Helminger Stefanie N. Lindstaedt Christian Rechberger A. Trugler 32 8 0 24 May 2022
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning Harsh Chaudhari Matthew Jagielski Alina Oprea 28 7 0 20 May 2022
Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning Ziyao Liu Jiale Guo Kwok-Yan Lam Jun Zhao 12 80 0 31 Mar 2022
SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service Songlei Wang Yifeng Zheng Xiaohua Jia GNN 19 22 0 16 Feb 2022
CECILIA: Comprehensive Secure Machine Learning Framework Ali Burak Ünal Nícolas Pfeifer Mete Akgun 25 2 0 07 Feb 2022
Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics T. Ryffel Francis R. Bach D. Pointcheval 21 21 0 28 Jan 2022
Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context J. Zalonis Frederik Armknecht Björn Grohmann Manuel Koch 25 4 0 27 Jan 2022
SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation Yash More Prashanthi Ramachandran Priyam Panda A. Mondal Harpreet Virk Debayan Gupta FedML 19 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
Morse-STF: Improved Protocols for Privacy-Preserving Machine Learning Qizhi Zhang Sijun Tan Lichun Li Yun Zhao Dong Yin Shan Yin 23 1 0 24 Sep 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 38 16 0 20 Sep 2021
CrypTen: Secure Multi-Party Computation Meets Machine Learning Brian Knott Shobha Venkataraman Awni Y. Hannun Shubho Sengupta Mark Ibrahim L. V. D. van der Maaten 20 346 0 02 Sep 2021
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 29 100 0 10 Aug 2021
Secure Quantized Training for Deep Learning Marcel Keller Ke Sun MQ 16 65 0 01 Jul 2021
Circa: Stochastic ReLUs for Private Deep Learning Zahra Ghodsi N. Jha Brandon Reagen S. Garg 24 34 0 15 Jun 2021
Tetrad: Actively Secure 4PC for Secure Training and Inference Nishat Koti A. Patra Rahul Rachuri Ajith Suresh 12 69 0 05 Jun 2021
Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation Nuttapong Attrapadung Koki Hamada Dai Ikarashi Ryo Kikuchi Takahiro Matsuda Ibuki Mishina Hiraku Morita Jacob C. N. Schuldt 14 27 0 04 Jun 2021
Differential Privacy for Text Analytics via Natural Text Sanitization Xiang Yue Minxin Du Tianhao Wang Yaliang Li Huan Sun Sherman S. M. Chow 16 84 0 02 Jun 2021
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 57 183 0 22 Apr 2021
Practical Two-party Privacy-preserving Neural Network Based on Secret Sharing ZhengQiang Ge Zhipeng Zhou Dong Guo Qiang Li FedML 8 4 0 10 Apr 2021
Privacy-Preserving Video Classification with Convolutional Neural Networks Sikha Pentyala Rafael Dowsley Martine De Cock PICV 19 21 0 06 Feb 2021
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation Lushan Song Guopeng Lin Jiaxuan Wang Haoqi Wu Wenqiang Ruan Weili Han 29 9 0 06 Dec 2020
Effectiveness of MPC-friendly Softmax Replacement Marcel Keller Ke Sun 11 10 0 23 Nov 2020
Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation Approach Daniel H. Ramirez J. M. Aunón 4 7 0 22 Sep 2020
Accelerating 2PC-based ML with Limited Trusted Hardware M. Nawaz Aditya Gulati Kunlong Liu Vishwajeet Agrawal P. Ananth Trinabh Gupta 11 2 0 11 Sep 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
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning Weizhe Hua M. Umar Zhiru Zhang G. E. Suh FedML 16 29 0 26 Aug 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
MGX: Near-Zero Overhead Memory Protection for Data-Intensive Accelerators Weizhe Hua M. Umar Zhiru Zhang G. E. Suh GNN 36 19 0 20 Apr 2020
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy Fatemehsadat Mireshghallah Mohammadkazem Taram A. Jalali Ahmed T. Elthakeb Dean Tullsen H. Esmaeilzadeh 6 12 0 26 Mar 2020
CrypTFlow: Secure TensorFlow Inference Nishant Kumar Mayank Rathee Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 99 235 0 16 Sep 2019