Understanding source location privacy protocols in sensor networks via perturbation of Time Series
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6024
- Type
- E - Conference contribution
- DOI
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10.1109/INFOCOM.2017.8057122
- Title of conference / published proceedings
- IEEE International Conference on Computer Communications
- First page
- 1
- Volume
- 2017
- Issue
- -
- ISSN
- 0743-166X
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- 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)
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D - Data Science, Systems and Security
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in one of the two top conferences in networking, this work was the first to model a source location privacy (SLP) preserving routing protocol as a time series. It proposed a provably correct algorithm, based on information theoretic concepts, to transform the time series into a corresponding SLP-optimal noisy time series. The paper received a best-in-session award and contributed towards Warwick being awarded an NCSC-EPSRC Academic Centre of Excellence in Cyber Security Research. It has seen impact in further work on SLP (Mutalemwa, Sensors 2019) and in privacy in social networks using trace adaptations (Zhao, IEEE/ACM ToN 2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -