Conservative or liberal? : personalized differential privacy
- Submitting institution
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The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5963
- Type
- E - Conference contribution
- DOI
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10.1109/ICDE.2015.7113353
- Title of conference / published proceedings
- 31st IEEE International Conference on Data Engineering (2015)
- First page
- 1023
- Volume
- -
- Issue
- -
- ISSN
- 1063-6382
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2015
- 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
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2
- Research group(s)
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D - Data Science, Systems and Security
- Citation count
- 52
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research is acknowledged as introducing and defining the important notion of "personalized differential privacy" in several subsequent works which build upon it, including on: releasing spatial data under (personalized) local differential privacy from Samsung Research (Chen, Li, Qin, Kasiviswanathan, Jin, IEEE ICDE); results casting differential privacy in the context of information theory from Princeton (Cuff, Yu, ACM CCS); models for pricing private information to create a personal data market from Kyoto (Nget, Cao, Yoshikawa, SIGIR eCOM 2017); and new partitioning-based algorithms to achieve personalized differential privacy from UCSD (Li, Xiong, Ji, Jiang, Advances in Knowledge Discovery and Data Mining).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -