The Johnson-Lindenstrauss transform itself preserves differential privacy

Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS

Volumn , Issue , 2012, Pages 410-419

  Blocki, Jeremiah ^a   Blum, Avrim ^a   Datta, Anupam ^a   Sheffet, Or ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Differential privacy; Graph cuts

Indexed keywords

DIFFERENTIAL PRIVACIES; GAUSSIANS; GRAPH CUT; JOHNSON-LINDENSTRAUSS TRANSFORMS; RANDOM NOISE; SINGULAR VALUES; TWO-GRAPHS;

COMPUTER SCIENCE; COVARIANCE MATRIX;

ALGORITHMS;

EID: 84871941776     PISSN: 02725428     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FOCS.2012.67     Document Type: Conference Paper

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