Quadratic Functional Encryption for Secure Training in Vertical Federated Learning

2023-05-15 · Shuangyi Chen, Anuja Modi, Shweta Agrawal, Ashish Khisti

Vertical federated learning (VFL) enables the collaborative training of machine learning (ML) models in settings where the data is distributed amongst multiple parties who wish to protect the privacy of their individual data. Notably, in VFL, the labels are available to a single party and the complete feature set is formed only when data from all parties is combined. Recently, Xu et al. proposed a new framework called FedV for secure gradient computation for VFL using multi-input functional encryption. In this work, we explain how some of the information leakage in Xu et al. can be avoided by using Quadratic functional encryption when training generalized linear models for vertical federated learning.

📄 PDF Abstract BibTeX arXiv:2305.08358

Code (0)

등록된 구현이 없습니다.

Tasks

Federated LearningVertical Federated Learning

Partially Encrypted Machine Learning using Functional Encryption

2019-05-24 · Theo Ryffel, Edouard Dufour-Sans, Romain Gay, Francis Bach 외

Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers witho…

BIG-bench Machine Learning Privacy Preserving

Partially Encrypted Deep Learning using Functional Encryption

2019-12-01 · NeurIPS 2019 12 · Théo Ryffel, David Pointcheval, Francis Bach, Edouard Dufour-Sans 외

BIG-bench Machine Learning Deep Learning Privacy Preserving

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges

2022-04-11 · Prajwal Panzade, Daniel Takabi

With the advent of functional encryption, new possibilities for computation on encrypted data have arisen. Functional Encryption enables data owners to grant third-party access to perform specified computations without d…

BIG-bench Machine Learning Cloud Computing Privacy Preserving

Efficient Vertical Federated Learning with Secure Aggregation

2023-05-18 · Xinchi Qiu, Heng Pan, Wanru Zhao, Chenyang Ma 외

The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can train complete models independently. However…

Federated Learning Fraud Detection Privacy Preserving Vertical Federated Learning

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations

2025-09-25 · Alexandru Ioniţă, Andreea Ioniţă arxiv

With the increased interest in artificial intelligence, Machine Learning as a Service provides the infrastructure in the Cloud for easy training, testing, and deploying models. However, these systems have a major privacy…