Top-k frequent itemsets via differentially private FP-trees

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2014, Pages 931-940

  Lee, Jaewoo ^a   Clifton, Christopher W ^a  

^a Purdue University   (United States)

Author keywords

differential privacy; fp tree; frequent itemset

Indexed keywords

DATA MINING; FORESTRY; PRIVATIZATION;

BACKGROUND INFORMATION; CORE DATUM; DIFFERENTIAL PRIVACIES; FP TREE; FREQUENT ITEMSET; FREQUENT ITEMSET MINING; ITEM SETS; PRIVACY THREATS;

TREES (MATHEMATICS);

ALGORITHMS; DATA PROCESSING; FORESTRY;

EID: 84907022968     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2623330.2623723     Document Type: Conference Paper

References (19)

1. C. C. Aggarwal, J. Pei, and B. Zhang. On privacy preservation against adversarial data mining. In KDD 2006, pages 510-516, New York, NY, USA, 2006. ACM.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB '94, pages 487-499, San Francisco, CA, USA, 1994. Morgan Kaufmann Publishers Inc.
3. M. Atzori, F. Bonchi, F. Giannotti, and D. Pedreschi. Anonymity preserving pattern discovery. The VLDB Journal, 17(4):703-727, July 2008.
4. R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta. Discovering frequent patterns in sensitive data. In KDD 2010, pages 503-512, New York, NY, USA, 2010. ACM.
5. J. Chen and K. Xiao. Bisc: A bitmap itemset support counting approach for efficient frequent itemset mining. ACM Trans. Knowl. Discov. Data, 4(3):12:1-12:37, Oct. 2010.
6. B. Ding, M. Winslett, J. Han, and Z. Li. Differentially private data cubes: optimizing noise sources and consistency. In SIGMOD 2011, pages 217-228, New York, NY, USA, 2011. ACM.
7. C. Dwork. Differential privacy. In ICALP 2006, pages 1-12, July 9-16 2006.
8. C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In TCC 2006, pages 265-284. Springer, 2006.
9. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. SIGMOD Rec., 29(2):1-12, May 2000.
10. M. Hardt. A Study of Privacy and Fairness in Sensitive Data Analysis. PhD thesis, Princeton University, 2011.
11. M. Hardt and G. N. Rothblum. A multiplicative weights mechanism for privacy-preserving data analysis. In FOCS 2010, pages 61-70, Washington, DC, USA, 2010. IEEE Computer Society.
12. M. Hay, V. Rastogi, G. Miklau, and D. Suciu. Boosting the accuracy of differentially private histograms through consistency. Proc. VLDB Endow., 3(1-2):1021-1032, Sept. 2010.
13. M. Kantarcioǧlu, J. Jin, and C. Clifton. When do data mining results violate privacy? In KDD 2004, pages 599-604, New York, NY, USA, 2004. ACM.
14. C. L. Lawson and R. J. Hanson. Solving least squares problems. Classics in applied mathematics. SIAM, Philadelphia, PA, 3 edition, 1995.
15. N. Li, W. Qardaji, D. Su, and J. Cao. Privbasis: frequent itemset mining with differential privacy. Proc. VLDB Endow., 5(11):1340-1351, July 2012.
16. F. D. McSherry. Privacy integrated queries: An extensible platform for privacy-preserving data analysis. In SIGMOD 2009, pages 19-30, New York, NY, USA, 2009. ACM.
17. A. Roth and T. Roughgarden. Interactive privacy via the median mechanism. In STOC 2010, pages 765-774, New York, NY, USA, 2010. ACM.
18. M. J. Zaki. Scalable algorithms for association mining. IEEE Trans. on Knowl. and Data Eng., 12(3):372-390, May 2000.
19. C. Zeng, J. F. Naughton, and J.-Y. Cai. On differentially private frequent itemset mining. Proc. VLDB Endow., 6(1):25-36, Nov. 2012.