On-the-fly privacy for location histograms
- Submitting institution
-
Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 103369684
- Type
- D - Journal article
- DOI
-
10.1109/TDSC.2020.2980270
- Title of journal
- IEEE Transactions on Dependable and Secure Computing
- Article number
- -
- First page
- 0
- Volume
- 0
- Issue
- -
- ISSN
- 1545-5971
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2020
- URL
-
http://dx.doi.org/10.1109/TDSC.2020.2980270
- 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
-
3
- Research group(s)
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C - Cybersecurity, privacy and human centred computing
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper that proposes and implements on-the-fly privacy for location histograms. Histogram privacy protects the frequency of location visits rather than each visit independently and helps mitigate the important privacy threat of profiling mobile users via their location visit frequencies. The popular notion of differential privacy works by hiding the presence of an individual who is within a dataset of many users. In contrast, we protect an individual’s transmitting locations on the fly (e.g. via their smartphone), independently of the existence of other users. In this context, protection against profiling has not been explored.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -