Privacy-Preserving Distributed Linear Regression on High-Dimensional Data
Adrià Gascón,Phillipp Schoppmann,Borja Balle,Mariana Raykova,Jack Doerner,Samee Zahur,David Evans +6 more
- Vol. 2017, Iss: 4, pp 345-364
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TLDR
A hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products is proposed, suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers.Abstract:
We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD) algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.read more
Citations
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Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data
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PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks
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TL;DR: In this paper, the authors proposed PrivFL, a privacy-preserving system for training linear and logistic regression models and oblivious predictions in the federated setting, while guaranteeing data and model privacy as well as ensuring robustness to users dropping out.
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Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning.
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Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
Nuria Rodríguez-Barroso,Goran Stipcich,Daniel Jiménez-López,José Antonio Ruiz-Millán,Eugenio Martínez-Cámara,Gerardo González-Seco,M. Victoria Luzón,Miguel Angel Veganzones,Francisco López Herrera +8 more
TL;DR: In this paper, the authors present the http URL Federated Learning framework that is built upon an holistic view of federated learning and differential privacy, and the definition of methodological guidelines for developing artificial intelligence services based on Federated learning.
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