Lattice_based__Privacy_preserving_Scalar_Product_Computation.pdf (1.28 MB)
Scalar product lattice computation for efficient privacy-preserving systems
journal contribution
posted on 2020-08-04, 12:41 authored by Yogachandran RahulamathavanYogachandran Rahulamathavan, Safak DoganSafak Dogan, Xiyu ShiXiyu Shi, Rongxing Lu, Muttukrishnan Rajarajan, Ahmet KondozPrivacy-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 JournalVolume
8Issue
3Pages
1417 - 1427Publisher
Institute of Electrical and Electronics EngineersVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher 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
2020-07-25Publication date
2020-08-06ISSN
2327-4662Publisher version
Language
- en