You are looking at a specific version 20210803:122847 of this paper. See the latest version.

Paper 2019/354

Benchmarking Privacy Preserving Scientific Operations

Abdelrahaman Aly and Nigel P. Smart

Abstract

In this work, we examine the efficiency of protocols for secure evaluation of basic mathematical functions ($\mathtt{sqrt}, \mathtt{sin}, \mathtt{arcsin}$, amongst others), essential to various application domains. e.g., Artificial Intelligence. Furthermore, we have incorporated our code in state-of-the-art Multiparty Computation (MPC) software, so we can focus on the algorithms to be used as opposed to the underlying MPC system. We make use of practical approaches that, although, some of them, theoretically can be regarded as less efficient, can, nonetheless, be implemented in such software libraries without further adaptation. We focus on basic scientific operations, and introduce a series of data-oblivious protocols based on fixed point representation techniques. Our protocols do not reveal intermediate values and do not need special adaptations from the underlying MPC protocols. We include extensive computational experimentation under various settings and MPC protocols.

Note: minor typos fixed on algorithm app_sq

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
Published elsewhere. Minor revision. ACNS 2019
Contact author(s)
nigel smart @ kuleuven be,Abdelrahaman Aly @ esat kuleuven be
History
2021-08-03: last of 2 revisions
2019-04-03: received
See all versions
Short URL
https://ia.cr/2019/354
License
Creative Commons Attribution
CC BY
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.