Privacy-preserving outlier detection through random nonlinear data distortion

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 41, Issue 1, 2011, Pages 260-272

  Bhaduri, Kanishka ^a   Stefanski, Mark D ^b   Srivastava, Ashok N ^a  

^a NASA AMES RESEARCH CENTER   (United States)

^b Stanford University   (United States)

Author keywords

Data mining; non linear; perturbation; privacy preserving

Indexed keywords

ANOMALY DETECTION; DATA MINERS; DEGREE OF NON-LINEARITY; INVERTIBILITY; LINEAR MODEL; NON-LINEAR; NON-LINEAR MODEL; NON-LINEAR TRANSFORMATIONS; NONLINEAR DATA; OUTLIER DETECTION; PERTURBATION; PRIVACY PRESERVATION; PRIVACY PRESERVING DATA MINING; PRIVACY-PRESERVING; RANDOM DATA; REAL LIFE DATASETS; SENSITIVE DATAS; SENSITIVE INFORMATIONS; SIGMOID FUNCTION; SPECIFIC PROBLEMS; STANDARD DEFINITIONS;

DATA MINING; LINEAR TRANSFORMATIONS; METADATA; MINERS;

DATA PRIVACY;

EID: 79551683372     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2010.2051540     Document Type: Article

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