Differentially Private High-Dimensional Data Publication in Internet of Things
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2543
- Type
- D - Journal article
- DOI
-
10.1109/jiot.2019.2955503
- Title of journal
- IEEE Internet of Things Journal
- Article number
- -
- First page
- 2640
- Volume
- 7
- Issue
- 4
- ISSN
- 2327-4662
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2019
- URL
-
https://e-space.mmu.ac.uk/624537/
- 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
-
5
- Research group(s)
-
D - Smart Infrastructure
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work proposes the compressed sensing mechanism (CSM), a novel optimisation framework that addresses privacy issues in publishing high-dimensional data. It is the first research to use four real-world high-dimensional datasets for experimentations: 1. AOL (45 attributes), 2. Retail (50 attributes), 3. UCI Adult (45222 individuals’ data), and 4. TPC-E (40,000 cardinalities, 24 dimensions, and domain size of 277). The experiments underlined that the CSM outperforms existing ‘state-of-the-art’ differentially private mechanisms for high-dimensional data performance by some magnitude. The work was funded by the National Natural Science Foundation of China.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -