An Identity Privacy Preserving IoT Data Protection Scheme for Cloud Based Analytics
(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.
(Less)
- author
- Gehrmann, Christian LU and Gunnarsson, Martin LU
- organization
- publishing date
- 2019
- 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}}, }