The DOI matches its Crossref record, with a small difference in title or year; 48 of the 50 references it cites were found in a registry or an index.
A caching and spatialK-anonymity driven privacy enhancement scheme in continuous location-based services
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In the citing paper
“This architecture, also known as a user-centralized architecture [128], assures better privacy than centralized RS, as users actively manage their data during the recommendation process [8].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [128]
In the citing paper
“Common anonymization methods include k-anonymity, l-diversity, and t-closeness [128, 159, 160].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [128]
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Metadata
- Authors
- Zhang, S., Li, X., Tan, Z., Peng, T., & Wang, G.
- Affiliations
- Hunan University of Science and Technology (CN)
- Edinburgh Napier University (GB)
- Guangzhou University (CN)
- Corresponding author
- Wang, Guojun
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 94
- Pages
- 40-50
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2900467432
- Cited by
- 163
- References
- 50
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 17 · 2023: 19 · 2022: 28 · 2021: 26 · 2020: 36 · 2019: 29
- FWCI
- 12.88
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Future Generation Computer Systems (publishedVersion)
- Research Output (Edinburgh Napier University) (submittedVersion, cc-by-nc-nd)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (61632009, 61772194, 61802076)
- Hunan Provincial Education Department of China (16A115)
- Hunan Provincial Natural Science Foundation of China (2017JJ2100, 2018JJ3190, 2018JJ3191)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- location privacy, location-based services, query result caching, multi-level caching, user mobility prediction, Markov model, cache hit rate, data freshness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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