Secure Multiparty Computation

Secure multiparty computation (MPC) enables collaborative computation on sensitive data held by multiple parties without revealing individual inputs. Current research heavily focuses on applying MPC to machine learning, particularly deep learning and federated learning, often incorporating techniques like differential privacy and quantization to enhance security and mitigate vulnerabilities like poisoning attacks and inference attacks. This field is crucial for addressing privacy concerns in data-driven applications, enabling collaborative model training and analysis across distributed datasets while maintaining confidentiality and data integrity. The resulting advancements are impacting various sectors, including healthcare, finance, and other areas where sensitive data is involved.

Papers

August 10, 2024

Quantum-secure multiparty deep learning
Kfir Sulimany, Sri Krishna Vadlamani, Ryan Hamerly, Prahlad Iyengar, Dirk Englund
Deep Learning Neural Network Weight Deep Learning Inference Secure Multiparty Computation Practical Quantum Secure Cloud

June 26, 2024

A Quantization-based Technique for Privacy Preserving Distributed Learning
Maurizio Colombo, Rasool Asal, Ernesto Damiani, Lamees Mahmoud AlQassem, Al Anoud Almemari, Yousof Alhammadi
LeArning Abstract Differential Privacy Privacy Preserving Machine Learning Architecture Secure Multiparty Computation

April 27, 2024

Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness
Ali Reza Ghavamipour, Benjamin Zi Hao Zhao, Oguzhan Ersoy, Fatih Turkmen
Decentralized Learning Data Aggregation Decentralized Machine Learning Secure Multiparty Computation

April 16, 2024

Confidential Federated Computations
Hubert Eichner, Daniel Ramage, Kallista Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albert Cheu, Katharine Daly, Adria Gascon, Marco Gruteser, Brendan McMahan
Differential Privacy Privacy Guarantee Secure Aggregation Federated Setting Privacy Sensitive Secure Multiparty Computation

March 17, 2024

Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption
Xuanqi Liu, Zhuotao Liu, Qi Li, Ke Xu, Mingwei Xu
Homomorphic Encryption Collaborative Learning Privacy Preserving Machine Learning Private Model Training Model Privacy Secure Multiparty Computation Tacit Collusion Pen Like Object

November 7, 2023

SaFL: Sybil-aware Federated Learning with Application to Face Recognition
Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez, Ruben Tolosana, Aythami Morales
Application Proficiency Federated Learning Recognition Rate Homomorphic Encryption Secure Aggregation Secure Multiparty Computation

June 3, 2023

Over-the-Air Federated Learning In Broadband Communication
Wayne Lemieux, Raphael Pinard, Mitra Hassani
Differential Privacy Multiple Input Multiple Output Inference Attack Air Federated Learning Secure Multiparty Computation

March 29, 2023

Federated Learning in MIMO Satellite Broadcast System
Raphael Pinard, Mitra Hassani, Wayne Lemieux
Differential Privacy Scientific Inference Multiple Input Multiple Output Secure Multiparty Computation

December 8, 2022

Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy
Ergute Bao, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin Hong Meng Tan, Khin Mi Mi Aung
Differential Privacy Novel Approach Privacy Guarantee Secure Multiparty Computation Privacy Accounting Critical Mixture Ratio

October 26, 2022

Privacy-preserving Automatic Speaker Diarization
Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso
Speaker Diarization Encryption Method Secure Multiparty Computation Sensitive Hashing

October 13, 2022

Secure Multiparty Computation for Synthetic Data Generation from Distributed Data
Mayana Pereira, Sikha Pentyala, Anderson Nascimento, Rafael T. de Sousa, Martine De Cock
Synthetic Data Synthetic Data Generation Data Distribution Private Synthetic Data Generation Secure Multiparty Computation Multi Party Computation

October 12, 2022

Privacy of federated QR decomposition using additive secure multiparty computation
Anne Hartebrodt, Richard Röttger
Privacy Preserving Privacy Policy Secure Multiparty Computation QR Decomposition

August 21, 2022

Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images
S. Maryam Hosseini, Milad Sikaroudi, Morteza Babaei, H. R. Tizhoosh
Differential Privacy Histopathology Image Cluster Structure Secure Approach Private Training Secure Multiparty Computation Party Computation Patient Privacy

June 24, 2022

MPClan: Protocol Suite for Privacy-Conscious Computations
Nishat Koti, Shravani Patil, Arpita Patra, Ajith Suresh
Data Privacy Population Protocol Privacy Preserving Computation Secure Multiparty Computation Cryptographic Protocol Party Protocol

May 23, 2022

PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
Sikha Pentyala, Nicola Neophytou, Anderson Nascimento, Martine De Cock, Golnoosh Farnadi
Differential Privacy Group Fairness Fair Machine Learning Secure Multiparty Computation

May 20, 2022

SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning
Harsh Chaudhari, Matthew Jagielski, Alina Oprea
Diverse Ensemble Private Learning Model Ensemble Adversarial Machine Learning Secure Multiparty Computation Multi Party Computation

April 11, 2022

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges
Prajwal Panzade, Daniel Takabi
Technical Challenge Emerging Opportunity Homomorphic Encryption Privacy Preserving Machine Learning Secure Multiparty Computation Functional Encryption

February 22, 2022

Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation
Kunal Talwar
Financial Application Differential Privacy Data Distribution Private Learning Secure Multiparty Computation Privacy Model Privacy Preserving Summation

February 5, 2022

Training Differentially Private Models with Secure Multiparty Computation
Sikha Pentyala, Davis Railsback, Ricardo Maia, Rafael Dowsley, David Melanson, Anderson Nascimento, Martine De Cock
Training Data Differential Privacy Privacy Guarantee Private Model Secure Multiparty Computation Multi Party Computation