On differentially private low rank approximation

Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

Volumn , Issue , 2013, Pages 1395-1414

  Kapralov, Michael ^a   Talwar, Kunal ^b  

^a STANFORD UNIVERSITY   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

POLYNOMIAL APPROXIMATION;

DIFFERENTIAL PRIVACIES; INPUT MATRICES; LOW RANK APPROXIMATIONS; POLYNOMIAL-TIME; POLYNOMIAL-TIME ALGORITHMS; PRINCIPAL EIGEN-VECTOR; SENSITIVE INFORMATIONS; SYMMETRIC MATRICES;

APPROXIMATION ALGORITHMS;

EID: 84876059763     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611973105.101     Document Type: Conference Paper

References (17)

1. P.-A. Absil, A. Edelman, and P. Koev. On the largest principal angle between random subspaces. Linear Algebra Appl, 2005.
2. K. Ball. An elementary introduction to modern convex geometry. Flavors of Geometry, MSRI Publications, 1997.
3. Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet. The johnson-lindenstrauss transform itself preserves differential privacy. FOCS, 2012.
4. Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim. Practical privacy: the sulq framework. In Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, PODS '05, pages 128-138, New York, NY, USA, 2005. ACM.
5. Joseph A. Calandrino, Ann Kilzer, Arvind Narayanan, Edward W. Feiten, and Vitaly Shmatikov. "You Might Also Like:" Privacy Risks of Collaborative Filtering. In IEEE Symposium on Security and Privacy, pages 231-246, 2011.
6. Kamalika Chaudhuri, Anand Sarwate, and Kaushik Sinha. Near-optimal algorithms for differentially private principal components. In Neural Information Processing Systems (NIPS), 2012. To appear.
7. Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. Our data, ourselves: Privacy via distributed noise generation. In EUROCRYPT, pages 486-503, 2006.
8. Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Proceedings of the Third conference on Theory of Cryptography, TCC'06, pages 265-284, Berlin, Heidelberg, 2006. Springer-Verlag.
9. Anupam Gupta, Aaron Roth, and Jonathan Ullman. Iterative constructions and private data release. In Proceedings of the 9th international conference on Theory of Cryptography, TCC'12, pages 339-356, Berlin, Heidelberg, 2012. Springer-Verlag.
10. Moritz Hardt and Aaron Roth. Beating randomized response on incoherent matrices. STOC, pages 1255-1268, 2012.
11. László Lovász and Santosh Vempala. Fast algorithms for logconcave functions: Sampling, rounding, integration and optimization. FOCS, pages 57-68, 2006.
12. Frank McSherry and Ilya Mironov. Differentially private recommender systems: building privacy into the net. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 627-636, New York, NY, USA, 2009. ACM.
13. Frank McSherry and Kunal Talwar. Mechanism design via differential privacy. FOCS, pages 94-103, 2007.
14. R. Muirhead. Aspects of multivariate statistical theory. Wiley Series in Probability and Mathematical Statistics, 1982.
15. Arvind Narayanan and Vitaly Shmatikov. Robust Deanonymization of Large Sparse Datasets. In IEEE Symposium on Security and Privacy, pages 111-125, 2008.
16. Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. Smooth sensitivity and sampling in private data analysis. In ACM Symposium on Theory of Computing, pages 75-84, 2007.
17. Alan Weinstein. Almost invariant submanifolds for compact group actions. J. Eur. Math. Soc. (JEMS), 2:53-86, 2000.