SEEN: A Selective Encryption Method to Ensure Confidentiality for Big Sensing Data Streams
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 261910-263350-1292
- Type
- D - Journal article
- DOI
-
10.1109/TBDATA.2017.2702172
- Title of journal
- IEEE Transactions on Big Data
- Article number
- -
- First page
- 379
- Volume
- 5
- Issue
- 3
- ISSN
- 2332-7790
- Open access status
- Deposit exception
- Month of publication
- May
- Year of publication
- 2017
- URL
-
https://doi.org/10.1109/TBDATA.2017.2702172
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
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F - Networked and Ubiquitous Systems Engineering (NUSE)
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces the first streaming data confidentiality approach derived from user requirements combined with data sensitivity levels for dynamic encryption in real-time data streams. Multilevel data confidentiality in streams make the contribution unique, where state-of-the-art works on data at rest. It provides a strong foundation to design and implement real-time monitoring applications. A theoretical proof validates that insider and outsider attackers cannot break the proposed system. Testbed and simulation results validate that system security is maintained without degrading system scalability, a key requirement for industry scale roll-out. This paper received 26 citations (Google Scholar).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -