Distributed computing over encrypted data

Nikolaos M. Freris, Panagiotis Patrinos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a new theme for performing computations directly on encrypted data: the concept of homomorphic encryption, i.e., cryptosystems that allow a user to manipulate encrypted information even without knowing the secret key. In an attempt to alleviate the gap between cryptography which naturally operates on rings, groups, and fields, and signal processing which typically operates on real(complex)-valued data, we set the stage for distributed operations on encrypted data. We leverage advances in homomorphic encryption (such as the RSA, Paillier and Gentry cryptosystems), and in quantized signal processing and consensus to devise encryption-friendly distributed computing primitives. In specific, we show how to perform encrypted average consensus with finite-time convergence, using modular multiplication and exponentiation on encrypted information. We present the architecture for secure cloud computing and discuss its advantages and applicability to a wide range of data mining, signal processing and control operations over the cloud.

Original languageEnglish (US)
Title of host publication54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1116-1122
Number of pages7
ISBN (Electronic)9781509045495
DOIs
StatePublished - Feb 10 2017
Event54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 - Monticello, United States
Duration: Sep 27 2016Sep 30 2016

Publication series

Name54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016

Other

Other54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
CountryUnited States
CityMonticello
Period9/27/169/30/16

Keywords

  • Cloud computing
  • Cryptography
  • Cyberphysical systems
  • Data mining
  • Distributed algorithms
  • Homomorphic encryption
  • Machine learning
  • Privacy
  • Quantized optimization
  • Security
  • Signal processing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Control and Optimization

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  • Cite this

    Freris, N. M., & Patrinos, P. (2017). Distributed computing over encrypted data. In 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 (pp. 1116-1122). [7852360] (54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ALLERTON.2016.7852360