On the public citation graph
Two-phase entropy based approach to big data anonymization
0 signed · 20 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/j.1538-7305.1948.tb01338.xopenalex-snapshot
A Mathematical Theory of Communication
inferredcites →https://doi.org/10.1016/j.jcss.2014.02.007openalex-snapshot
A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud
inferredcites →https://doi.org/10.1109/icde.2005.143openalex-snapshot
Top-Down Specialization for Information and Privacy Preservation
inferredcites →https://doi.org/10.1109/icde.2006.1openalex-snapshot
L-diversity: privacy beyond k-anonymity
inferredcites →https://doi.org/10.1109/icdm.2004.10110openalex-snapshot
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
inferredcites →https://doi.org/10.1109/tpds.2013.48openalex-snapshot
A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud
inferredcites →https://doi.org/10.1109/wi-iat.2014.139openalex-snapshot
Protecting Data Privacy from Being Inferred from High Dimensional Correlated Data
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/10.1145/1233321.1233324openalex-snapshot
Utility-based anonymization for privacy preservation with less information loss
inferredcites →https://doi.org/10.1145/1749603.1749605openalex-snapshot
Privacy-preserving data publishing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4783417openalex-extracted
Enhancing Data Privacy in Substations: An Analysis of Data Sharing Anonymization for the Iec61850 Protocols, with a Special Focus on Goose
inferred← citeshttps://doi.org/10.1109/gcat52182.2021.9587765openalex-snapshot
Privacy Preserving Big Data mining using Pseudonymization and Homomorphic Encryption
inferred← citeshttps://doi.org/10.1109/ubmk.2017.8093543openalex-snapshot
Big data anonymization with spark
inferred← citeshttps://doi.org/10.1109/worlds450073.2020.9210353openalex-snapshot
Big data and Spark: Comparison with Hadoop
inferred← citeshttps://doi.org/10.13052/jwe1540-9589.20613openalex-snapshot
A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators
inferred← citeshttps://doi.org/10.17762/ijcnis.v14i1.5187openalex-snapshot
Improved Technique for Preserving Privacy while Mining Real Time Big Data
inferred← citeshttps://doi.org/10.2174/1574893614666190416152025openalex-snapshot
A Micro-Aggregation Algorithm Based on Density Partition Method for Anonymizing Biomedical Data
inferred← citeshttps://doi.org/10.5753/washes.2025.8180openalex-snapshot
Conformidade com os Requisitos Legais de Privacidade de Dados: Um Estudo sobre Técnicas de Anonimização
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.