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

Gehrmann, Christian LU and Gunnarsson, Martin LU (2019) 2019 IEEE International Conference on Big Data, Big Data 2019 In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 p.5744-5753
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... (More)

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.

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Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
analytics, identity privacy, IoT security
host publication
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
series title
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
editor
Baru, Chaitanya ; Huan, Jun ; Khan, Latifur ; Hu, Xiaohua Tony ; Ak, Ronay ; Tian, Yuanyuan ; Barga, Roger ; Zaniolo, Carlo ; Lee, Kisung and Ye, Yanfang Fanny
article number
9006017
pages
10 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2019 IEEE International Conference on Big Data, Big Data 2019
conference location
Los Angeles, United States
conference dates
2019-12-09 - 2019-12-12
external identifiers
  • scopus:85081402750
ISBN
9781728108582
DOI
10.1109/BigData47090.2019.9006017
project
Cyber Security for Next Generation Factory (SEC4FACTORY)
Cloudification of Production Engineering for Predictive Digital Manufacturing
language
English
LU publication?
yes
id
c8bbebe4-c5df-4ea8-b1f0-af2cf5dab62b
date added to LUP
2020-03-30 09:28:16
date last changed
2023-04-10 11:31:49
@inproceedings{c8bbebe4-c5df-4ea8-b1f0-af2cf5dab62b,
  abstract     = {{<p>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.</p>}},
  author       = {{Gehrmann, Christian and Gunnarsson, Martin}},
  booktitle    = {{Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019}},
  editor       = {{Baru, Chaitanya and Huan, Jun and Khan, Latifur and Hu, Xiaohua Tony and Ak, Ronay and Tian, Yuanyuan and Barga, Roger and Zaniolo, Carlo and Lee, Kisung and Ye, Yanfang Fanny}},
  isbn         = {{9781728108582}},
  keywords     = {{analytics; identity privacy; IoT security}},
  language     = {{eng}},
  pages        = {{5744--5753}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019}},
  title        = {{An Identity Privacy Preserving IoT Data Protection Scheme for Cloud Based Analytics}},
  url          = {{https://lup.lub.lu.se/search/files/81503431/PrivacyPerservingIoTDataEncryption.pdf}},
  doi          = {{10.1109/BigData47090.2019.9006017}},
  year         = {{2019}},
}