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BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-540-69497-7_9openalex-snapshot
Privacy-Preserving Publication of User Locations in the Proximity of Sensitive Sites
inferredcites →https://doi.org/10.1007/978-3-540-71703-4_18openalex-snapshot
Efficient k-Anonymization Using Clustering Techniques
inferredcites →https://doi.org/10.1007/978-3-540-73871-8_10openalex-snapshot
A k-Anonymity Clustering Method for Effective Data Privacy Preservation
inferredcites →https://doi.org/10.1007/978-3-642-15838-4_12openalex-snapshot
Semantic Microaggregation for the Anonymization of Query Logs
inferredcites →https://doi.org/10.1007/bfb0052867openalex-snapshot
The BANG-clustering system: Grid-based data analysis
inferredcites →https://doi.org/10.1007/s00778-014-0351-4openalex-snapshot
Enhancing data utility in differential privacy via microaggregation-based $$k$$ k -anonymity
inferredcites →https://doi.org/10.1016/j.ipm.2011.01.004openalex-snapshot
User k-anonymity for privacy preserving data mining of query logs
inferredcites →https://doi.org/10.1016/j.patrec.2004.09.052openalex-snapshot
GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases
inferredcites →https://doi.org/10.1109/icde.2005.42openalex-snapshot
Data Privacy through Optimal k-Anonymization
inferredcites →https://doi.org/10.1109/icde.2006.101openalex-snapshot
Mondrian Multidimensional K-Anonymity
inferredcites →https://doi.org/10.1109/icde.2007.367856openalex-snapshot
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
inferredcites →https://doi.org/10.1109/icde.2007.369024openalex-snapshot
K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization
inferredcites →https://doi.org/10.1109/icpr.1996.546732openalex-snapshot
Grid-clustering: an efficient hierarchical clustering method for very large data sets
inferredcites →https://doi.org/10.1109/sfcs.1995.492461openalex-snapshot
Private information retrieval
inferredcites →https://doi.org/10.1142/s0218488502001648openalex-snapshot
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
inferredcites →https://doi.org/10.1142/s021848850200165xopenalex-snapshot
ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION
inferredcites →https://doi.org/10.1145/1055558.1055591openalex-snapshot
On the complexity of optimal K-anonymity
inferredcites →https://doi.org/10.1145/1066116.1189037openalex-snapshot
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
inferredcites →https://doi.org/10.1145/1142351.1142374openalex-snapshot
Achieving anonymity via clustering
inferredcites →https://doi.org/10.1145/1142473.1142499openalex-snapshot
Injecting utility into anonymized datasets
inferredcites →https://doi.org/10.1145/1150402.1150504openalex-snapshot
Utility-based anonymization using local recoding
inferredcites →https://doi.org/10.1145/1376616.1376631openalex-snapshot
Private queries in location based services
inferredcites →https://doi.org/10.1145/1749603.1749605openalex-snapshot
Privacy-preserving data publishing
inferredcites →https://doi.org/10.1145/275487.275508openalex-snapshot
Generalizing data to provide anonymity when disclosing information (abstract)
inferredcites →https://doi.org/10.1145/38714.38743openalex-snapshot
The BANG file: A new kind of grid file
inferredcites →https://doi.org/10.5555/1182635.1164193openalex-snapshot
The new Casper: query processing for location services without compromising privacy
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Predictive Computing and Information Security: A Technical Review
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Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique
inferred← citeshttps://doi.org/10.1016/j.scs.2018.04.014openalex-snapshot
Privacy preserving data by conceptualizing smart cities using MIDR-Angelization
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