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Towards the Sign Function Best Approximation for Secure Outsourced Computations and Control

Author

Listed:
  • Mikhail Babenko

    (North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 1 Pushkin Street, 355017 Stavropol, Russia
    Control/Management and Applied Mathematics, Ivannikov Institute for System Programming, 109004 Moscow, Russia)

  • Andrei Tchernykh

    (Control/Management and Applied Mathematics, Ivannikov Institute for System Programming, 109004 Moscow, Russia
    Computer Science Department, CICESE Research Center, Ensenada 22800, Mexico
    School of Electronic Engineering and Computer Science, South Ural State University, 454080 Chelyabinsk, Russia)

  • Bernardo Pulido-Gaytan

    (Computer Science Department, CICESE Research Center, Ensenada 22800, Mexico)

  • Arutyun Avetisyan

    (Control/Management and Applied Mathematics, Ivannikov Institute for System Programming, 109004 Moscow, Russia)

  • Sergio Nesmachnow

    (Faculty of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

  • Xinheng Wang

    (Department of Mechatronics and Robotics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Fabrizio Granelli

    (Department of Information Engineering and Computer Science, University of Trento, 38150 Trento, Italy)

Abstract

Homomorphic encryption with the ability to compute over encrypted data without access to the secret key provides benefits for the secure and powerful computation, storage, and communication of resources in the cloud. One of its important applications is fast-growing robot control systems for building lightweight, low-cost, smarter robots with intelligent brains consisting of data centers, knowledge bases, task planners, deep learning, information processing, environment models, communication support, synchronous map construction and positioning, etc. It enables robots to be endowed with secure, powerful capabilities while reducing sizes and costs. Processing encrypted information using homomorphic ciphers uses the sign function polynomial approximation, which is a widely studied research field with many practical results. State-of-the-art works are mainly focused on finding the polynomial of best approximation of the sign function (PBAS) with the improved errors on the union of the intervals [ − 1 , − ϵ ] ∪ [ ϵ , 1 ] . However, even though the existence of the single PBAS with the minimum deviation is well known, its construction method on the complete interval [ − 1 , 1 ] is still an open problem. In this paper, we provide the PBAS construction method on the interval [ − 1 , 1 ] , using as a norm the area between the sign function and the polynomial and showing that for a polynomial degree n ≥ 1 , there is (1) unique PBAS of the odd sign function, (2) no PBAS of the general form sign function if n is odd, and (3) an uncountable set of PBAS, if n is even.

Suggested Citation

  • Mikhail Babenko & Andrei Tchernykh & Bernardo Pulido-Gaytan & Arutyun Avetisyan & Sergio Nesmachnow & Xinheng Wang & Fabrizio Granelli, 2022. "Towards the Sign Function Best Approximation for Secure Outsourced Computations and Control," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2006-:d:835967
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