A caching and spatialK-anonymity driven privacy enhancement scheme in continuous location-based services
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This architecture, also known as a user-centralized architecture, assures better privacy than centralized RS, as users actively manage their data during the recommendation process.
“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]
Common anonymization methods include k-anonymity, l-diversity, and t-closeness.
“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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MAD-RAPPEL: Mobility Aware Data Replacement And Prefetching Policy Enrooted LBS
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Location Privacy Protection Scheme for LBS in IoT
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Cache-Based Privacy Protection Scheme for Continuous Location Query
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- 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 · 2018: 1
- 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 › Signal Processing › 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
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