Breaching Euclidean distance-preserving data perturbation using few known inputs

Data and Knowledge Engineering

Volumn 83, Issue , 2013, Pages 93-110

  Giannella, Chris R ^a   Liu, Kun ^b   Kargupta, Hillol ^c,d  

^a MITRE CORPORATION   (United States)

^b LINKEDIN CORPORATION   (United States)

^c UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

^d AGNIK LLC   (United States)

Author keywords

Data mining; Data perturbation; Euclidean distance; Privacy

Indexed keywords

DATA MINING ALGORITHM; DATA PERTURBATION; DATA SETS; DATA TUPLES; ESTIMATION RESULTS; EUCLIDEAN; EUCLIDEAN DISTANCE; K-MEANS CLUSTERING; NUMBER OF DATUM; PRIVACY BREACHES; PRIVACY PRESERVING DATA MINING;

ALGORITHMS; DATA MINING; ESTIMATION;

DATA PRIVACY;

EID: 84871925104     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2012.10.004     Document Type: Article

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