Lattice_based__Privacy_preserving_Scalar_Product_Computation.pdf (1.28 MB)

Scalar product lattice computation for efficient privacy-preserving systems

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journal contribution
posted on 04.08.2020, 12:41 by Yogachandran Rahulamathavan, Safak Dogan, Xiyu Shi, Rongxing Lu, Muttukrishnan Rajarajan, Ahmet Kondoz
Privacy-preserving applications allow users to perform on-line daily actions without leaking sensitive information. The privacy-preserving scalar product is one of the critical algorithms in many private applications. The state-of-the-art privacy-preserving scalar product schemes use either computationally intensive homomorphic (public-key) encryption techniques such as Paillier encryption to achieve strong security (i.e., 128−bit) or random masking technique to achieve high efficiency for low security. In this paper, lattice structures have been exploited to develop an efficient privacy-preserving system. The proposed scheme is not only efficient in computation as compared to the state-of-the-art but also provides high degree of security against quantum attacks. Rigorous security and privacy analyses of the proposed scheme have been provided along with a concrete set of parameters to achieve 128−bit and 256 − bit security. Performance analysis shows that the scheme is at least five orders faster than the Paillier schemes and at least twice as faster than the existing randomisation technique at 128−bit security. Also the proposed scheme requires six-time fewer data compared to Paillier and randomisation based schemes for communications.

Funding

UK-India Education Research Initiative (UKIERI) through grant UGC-UKIERI-2016-17-019

History

School

  • Loughborough University London

Published in

IEEE Internet of Things

Publisher

Institute of Electrical and Electronics Engineers

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

25/07/2020

Publication date

2020-08-06

ISSN

2327-4662

Language

en

Depositor

Dr Yogachandran Rahulamathavan. Deposit date: 3 August 2020

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