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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A survey on privacy inference attacks and defenses in cloud-based Deep Neural Network
TL;DR: Wang et al. as discussed by the authors provided a taxonomy of privacy attacks and defenses based on the phases of the occurrence in the workflow of the cloud-based neural network, i.e., data manipulation, training and prediction.
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Vertical Federated Learning for Higher-Order Factorization Machines
TL;DR: Wang et al. as discussed by the authors proposed a lossless vertical federated learning (VFL) method for higher-order factorization machines (HOFMs), which takes into feature combinations efficiently and effectively and have succeeded in many tasks, especially recommender systems, link predictions, and natural language processing.
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Dynamic Games for Social Model Training Service Market via Federated Learning Approach
TL;DR: In this article , the authors model the users' participation in social model training as a training service market, which consists of model owners (MOs) as consumers who purchase the training service and a large number of mobile device groups (MDGs) as service providers who contribute local data in federated learning.
Journal ArticleDOI
Insuring against the perils in distributed learning: privacy-preserving empirical risk minimization.
TL;DR: Validation of the algorithm on real-world human recognition activity datasets establishes that the protocol incurs minimal computational overhead, provides substantial utility gains for typical security and privacy guarantees, and theoretically justifies the advantage of gradient perturbation in the proposed algorithm, therefore closing existing gap between practice and theory.
Journal Article
Communication Efficient Secure Logistic Regression
TL;DR: A new two-party construction for secure logistic regression training is presented, which enables two parties to train a logistics regression model on private secret shared data and develops many building blocks of independent interest, including a new approximation technique for the sigmoid function that results in a secure evaluation protocol with better communication.
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