Distributed computing over encrypted data

Nikolaos M. Freris; Panagiotis Patrinos

doi:10.1109/ALLERTON.2016.7852360

Distributed computing over encrypted data

Nikolaos M. Freris, Panagiotis Patrinos

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1116-1122
Number of pages	7
ISBN (Electronic)	9781509045495
DOIs	https://doi.org/10.1109/ALLERTON.2016.7852360
State	Published - Feb 10 2017
Event	54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 - Monticello, United States Duration: Sep 27 2016 → Sep 30 2016

Publication series

Name	54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016

Other

Other	54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
Country/Territory	United States
City	Monticello
Period	9/27/16 → 9/30/16

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

Access to Document

10.1109/ALLERTON.2016.7852360

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). Article 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

Distributed computing over encrypted data. / Freris, Nikolaos M.; Patrinos, Panagiotis.
54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1116-1122 7852360 (54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Freris, NM & Patrinos, P 2017, Distributed computing over encrypted data. in 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016., 7852360, 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1116-1122, 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016, Monticello, United States, 9/27/16. https://doi.org/10.1109/ALLERTON.2016.7852360

@inproceedings{2fcb03e608114e6b8e7fb18d95937db8,

title = "Distributed computing over encrypted data",

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.",

keywords = "Cloud computing, Cryptography, Cyberphysical systems, Data mining, Distributed algorithms, Homomorphic encryption, Machine learning, Privacy, Quantized optimization, Security, Signal processing",

author = "Freris, {Nikolaos M.} and Panagiotis Patrinos",

year = "2017",

month = feb,

day = "10",

doi = "10.1109/ALLERTON.2016.7852360",

language = "English (US)",

series = "54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1116--1122",

booktitle = "54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016",

note = "54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 ; Conference date: 27-09-2016 Through 30-09-2016",

}

TY - GEN

T1 - Distributed computing over encrypted data

AU - Freris, Nikolaos M.

AU - Patrinos, Panagiotis

PY - 2017/2/10

Y1 - 2017/2/10

N2 - 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.

AB - 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.

KW - Cloud computing

KW - Cryptography

KW - Cyberphysical systems

KW - Data mining

KW - Distributed algorithms

KW - Homomorphic encryption

KW - Machine learning

KW - Privacy

KW - Quantized optimization

KW - Security

KW - Signal processing

UR - http://www.scopus.com/inward/record.url?scp=85015147818&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015147818&partnerID=8YFLogxK

U2 - 10.1109/ALLERTON.2016.7852360

DO - 10.1109/ALLERTON.2016.7852360

M3 - Conference contribution

AN - SCOPUS:85015147818

T3 - 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016

SP - 1116

EP - 1122

BT - 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016

Y2 - 27 September 2016 through 30 September 2016

ER -

Distributed computing over encrypted data

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this