Fast computation of low-rank matrix approximations
Fast computation of low rank matrix approximations
Calibrating Noise to Sensitivity in Private Data Analysis
Practical privacy
Journal of the ACM
Lecture Notes in Computer Science
Proceedings of the thirty-third annual ACM symposium on Theory of computing - STOC ’01
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS ’05
Dimitris Achlioptas
Kobbi Nissim
Cynthia Dwork
Adam Smith
Avrim Blum
How robust are linear sketches to adaptive inputs?
Fast monte-carlo algorithms for finding low-rank approximations
Beyond worst-case analysis in private singular vector computation
Continuous matrix approximation on distributed data