Visible to the public Random Projection Data Perturbation Based Privacy Protection in WSNs

TitleRandom Projection Data Perturbation Based Privacy Protection in WSNs
Publication TypeConference Paper
Year of Publication2017
AuthorsMing, Z., Zheng-jiang, W., Liu, H.
Conference Name2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
PublisherIEEE
KeywordsAlgorithm design and analysis, Conferences, data fusion, data fusion algorithm, data fusion technology, Data integration, data privacy, data privacy protection mechanism, data traffic, network coverage area, Network reconnaissance, node privacy, PPND algorithm, pre-distribution random number, privacy preserving, privacy protection requirements, pubcrawl, random projection data perturbation based privacy protection, Resiliency, security, SMART algorithm, Sparse matrices, Sparse projection data perturbation, telecommunication security, Wireless sensor networks
Abstract

Wireless sensor networks are responsible for sensing, gathering and processing the information of the objects in the network coverage area. Basic data fusion technology generally does not provide data privacy protection mechanism, and the privacy protection mechanism in health care, military reconnaissance, smart home and other areas of the application is usually indispensable. In this paper, we consider the privacy, confidentiality, and the accuracy of fusion results, and propose a data fusion algorithm for privacy preserving. This algorithm relies on the characteristics of data fusion, and uses the method of pre-distribution random number in the node to get the privacy protection requirements of the original data. Theoretical analysis shows that the malicious attacker attempts to steal the difficulty of node privacy in PPND algorithm. At the same time in the TOSSIM simulation results also show that, compared with TAG, SMART algorithm, PPND algorithm in the data traffic, the convergence accuracy of the good performance.

URLhttp://ieeexplore.ieee.org/document/8116426/
DOI10.1109/INFCOMW.2017.8116426
Citation Keyming_random_2017