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
Reads0
Chats0
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
More filters
Book ChapterDOI
Efficient Secure Ridge Regression from Randomized Gaussian Elimination
TL;DR: In this paper, the authors present secure ridge regression using secure random self-reductions over prime fields, without the need for secure rational reconstruction at any stage as well as the use of secure fixed-point arithmetic.
Proceedings ArticleDOI
Horizontal Privacy-Preserving Linear Regression Which is Highly Efficient for Dataset of Low Dimension
Linpeng Lu,Ning Ding +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a new privacy-preserving linear regression protocol for the scenario where dataset is distributed horizontally, which works highly efficiently in particular when training dataset is of low dimension.
Book ChapterDOI
Learning Without Peeking: Secure Multi-party Computation Genetic Programming
TL;DR: An SMCGP approach based on the garbled circuit protocol is presented, which is evaluated using two problem sets: a widely studied symbolic regression benchmark, and a GP-based fault localisation technique with real world fault data from Defects4J benchmark.
Book ChapterDOI
A Practical Approach to the Secure Computation of the Moore–Penrose Pseudoinverse over the Rationals
Niek J. Bouman,Niels de Vreede +1 more
TL;DR: In this article, a secure multiparty computation (MPC) method for the case in which the rank of the system is unknown and should remain private is proposed, where the rank is assumed to be fixed.
Posted Content
Critical Overview of Privacy-Preserving Learning in Vector Autoregressive Models for Energy Forecasting.
TL;DR: The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as a trade-off between privacy and forecasting accuracy, while iterative fitting processes in which intermediate results are shared can be exploited so that the original data can be inferred after some iterations.
References
More filters
Book
Numerical Optimization
Jorge Nocedal,Stephen J. Wright +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Book
Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Book
The algebraic eigenvalue problem
TL;DR: Theoretical background Perturbation theory Error analysis Solution of linear algebraic equations Hermitian matrices Reduction of a general matrix to condensed form Eigenvalues of matrices of condensed forms The LR and QR algorithms Iterative methods Bibliography.
Book
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork,Aaron Roth +1 more
TL;DR: The preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example.