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An Identity Privacy Preserving IoT Data Protection Scheme for Cloud Based Analytics

Authors :
Christian Gehrmann
Martin Gunnarsson
Baru, Chaitanya
Huan, Jun
Khan, Latifur
Hu, Xiaohua Tony
Ak, Ronay
Tian, Yuanyuan
Barga, Roger
Zaniolo, Carlo
Lee, Kisung
Ye, Yanfang Fanny
Source :
2019 IEEE International Conference on Big Data (Big Data), IEEE BigData, Proceedings-2019 IEEE International Conference on Big Data, Big Data 2019; pp 5744-5753 (2019)
Publisher :
IEEE

Abstract

Efficient protection of huge amount of IoT produced data is key for wide scale data analytic services. The most efficient way is to use pure symmetric encryption as that allows both fast decryption at the analytic engine side as well as energy efficient encryption at the IoT side. However, symmetric encryption can only be performed if there is a way to directly map an encrypted object to the correct key. Typically, such mapping require a unique IoT identity, which constitute a privacy problem. In this paper, we present an IoT identity protection scheme for symmetric IoT data encryption. We give basic security definitions for this problem setting, present a new construction and give security proofs of security level achieved with the construction. Performance figures for a proof of concept implementation are also given. The new scheme gives a fair trade-off between identity privacy and complexity.

Details

Language :
English
ISBN :
978-1-72810-858-2
ISBNs :
9781728108582
Database :
OpenAIRE
Journal :
2019 IEEE International Conference on Big Data (Big Data), IEEE BigData, Proceedings-2019 IEEE International Conference on Big Data, Big Data 2019; pp 5744-5753 (2019)
Accession number :
edsair.doi.dedup.....976a9bdc99528b237b6fef29f0eb8f94
Full Text :
https://doi.org/10.1109/bigdata47090.2019.9006017