Optimization of privacy-utility trade-offs under informational self-determination
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-4062
- Type
- D - Journal article
- DOI
-
10.1016/j.future.2018.07.018
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 488
- Volume
- 109
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Compliant
- Month of publication
- July
- 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
-
1
- Research group(s)
-
E - DSS (Distributed Systems and Services)
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Introduces Pareto optimal trade-offs between privacy and accuracy in data sharing and analytics by optimizing over 20,000 differential privacy settings. For first time, these trade-offs are validated analytically and experimentally with real-world data for universal privacy settings among all users as well as for self-determined settings. Incentive models for certain privacy settings can provide to citizens’ data further (monetary) value. It empowers citizens to protect privacy by design without limiting their use of online services, while trust towards service providers is preserved. Applicable for COVID-19 health applications, power utilities and service providers performing collective measurements over citizens’ sensitive data.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
