Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud
- Submitting institution
-
City, University of London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 377
- Type
- D - Journal article
- DOI
-
10.1109/TDSC.2013.51
- Title of journal
- IEEE Transactions on Dependable and Secure Computing
- Article number
- -
- First page
- 467
- Volume
- 11
- Issue
- 5
- ISSN
- 1545-5971
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2014
- 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
-
4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper presents a new way of working on encrypted data that is uploaded into the Cloud. This scheme has been highlighted as the best to date to implement privacy preserving speech data in the Cloud and has received several Innovate UK innovation in cyber security awards. The outcome of this research has resulted in the formation of CityDefend, a cyber security start-up to protect customer data in the 3rd party cloud environments
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -