Projekt per år
Sammanfattning
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.
Originalspråk | engelska |
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Titel på värdpublikation | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
Redaktörer | Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye |
Förlag | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Sidor | 5744-5753 |
Antal sidor | 10 |
ISBN (elektroniskt) | 9781728108582 |
DOI | |
Status | Published - 2019 |
Evenemang | 2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, USA Varaktighet: 2019 dec. 9 → 2019 dec. 12 |
Publikationsserier
Namn | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
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Konferens
Konferens | 2019 IEEE International Conference on Big Data, Big Data 2019 |
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Land/Territorium | USA |
Ort | Los Angeles |
Period | 2019/12/09 → 2019/12/12 |
Ämnesklassifikation (UKÄ)
- Systemvetenskap, informationssystem och informatik
Fingeravtryck
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