Scalar Product Lattice Computation for Efficient Privacy-Preserving Systems
- 6 August 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Internet of Things Journal
- Vol. 8 (3) , 1417-1427
Abstract
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.Keywords
All Related Versions
Funding Information
- U.K.–India Education Research Initiative (UGC-UKIERI-2016-17-019)
This publication has 23 references indexed in Scilit:
- Outsourced Biometric Identification With PrivacyIEEE Transactions on Information Forensics and Security, 2018
- Privacy-Friendly Forecasting for the Smart Grid Using Homomorphic Encryption and the Group Method of Data Handling2017
- A Decade of Lattice CryptographyFoundations and Trends® in Theoretical Computer Science, 2016
- On the concrete hardness of Learning with ErrorsJournal of Mathematical Cryptology, 2015
- Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based ClassificationIEEE Journal of Biomedical and Health Informatics, 2014
- Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in CloudIEEE Transactions on Dependable and Secure Computing, 2013
- Encrypted signal processing for privacy protection: Conveying the utility of homomorphic encryption and multiparty computationIEEE Signal Processing Magazine, 2012
- Facial Expression Recognition in the Encrypted Domain Based on Local Fisher Discriminant AnalysisIEEE Transactions on Affective Computing, 2012
- Privacy-Preserving ECG Classification With Branching Programs and Neural NetworksIEEE Transactions on Information Forensics and Security, 2011
- On lattices, learning with errors, random linear codes, and cryptographyJournal of the ACM, 2009