LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 24398071
- Type
- D - Journal article
- DOI
-
10.1109/TIFS.2018.2812146
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 2151
- Volume
- 13
- Issue
- 9
- ISSN
- 1556-6013
- Open access status
- Compliant
- Month of publication
- March
- 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
-
6
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 43
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We propose a novel efficient privacy-preserving data publication scheme that achieves local privacy on high-dimensional crowdsourced systems. Our solution is highly efficient to build the correlations and joint distribution of attributes, which substantially outperforms the traditional Lasso and EM algorithms in terms of scalability, local privacy, pruning power, and accuracy. This research was funded under the EU 7th Framework Program. It has impacted on work by world-leading experts on information security, including Philip S. Yu (University of Illinons at Chicago), Julie McCann (Imperial College), Jiawei Han (University of Illinois Urbana-Champaign).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
