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
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
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Advances and Open Problems in Federated Learning
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References
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Book ChapterDOI
Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations
TL;DR: This work adds garbled circuits based IEEE 754 floating-point numbers to a secret sharing environment achieving very high efficiency and the first, to the authors' knowledge, fully IEEE 7 54 compliant secure floating- point implementation.
Posted Content
Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations.
TL;DR: In this article, Garbled circuits based IEEE 754 floating-point numbers are added to a secret sharing environment achieving very high efficiency and the first, to the best of our knowledge, fully IEEE754 compliant secure floating point implementation.
Posted Content
Faster Two-Party Computation Secure Against Malicious Adversaries in the Single-Execution Setting.
TL;DR: A new protocol for two-party computation, secure against malicious adversaries, that is significantly faster than prior work in the single-execution setting and requires only O(ρ) public key operations and ρ garbled circuits, where ρ is the statistical security parameter.
Proceedings ArticleDOI
Regression on distributed databases via secure multi-party computation
TL;DR: A method for performing linear regression on the union of distributed databases that does not entail constructing an integrated database, and therefore preserves confidentiality of the individual databases is presented.
Book ChapterDOI
Certificate Validation in Secure Computation and Its Use in Verifiable Linear Programming
TL;DR: This paper introduces certificate validation as an effective technique for achieving verifiable linear programming and designs particularly efficient distributed-prover zero-knowledge proofs for the validation of a certificate, fully exploiting the fact that ElGamal encryption can be used for this purpose.