Lightweight and privacy-friendly spatial data aggregation for secure power supply and demand management in smart grids
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5209
- Type
- D - Journal article
- DOI
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10.1109/tifs.2018.2881730
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 1554
- Volume
- 14
- Issue
- 6
- ISSN
- 1556-6013
- Open access status
- Technical exception
- Month of publication
- November
- Year of publication
- 2018
- URL
-
-
- 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
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1
- Research group(s)
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F - Security of Advanced Systems
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In existing masking-based data aggregation schemes, for demand-response and billing management in smart grids, a smart meter is not authenticated during data aggregation. Consequently, a dishonest or fake smart meter may falsify the data and cause an inaccurate aggregated result. In addition, current schemes cannot ensure resilience against collusion attacks (when a substation colludes with the power supplier in order to obtain individual usage data). This is the first paper to address all these issues. The analyses provided here show that the proposed solutions offer better privacy protection and computational efficiency than existing schemes.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -