Real-time aggregate monitoring with differential privacy

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 2169-2173

  Fan, Liyue ^a   Xiong, Li ^a  

^a EMORY UNIVERSITY   (United States)

Author keywords

differential privacy; estimation; sampling; time series

Indexed keywords

DATA DYNAMICS; DATA VALUES; DIFFERENTIAL PRIVACIES; NOISY OBSERVATIONS; PRIVATE DATA; REAL DATA SETS; SAMPLING POINTS; TIME STAMPS; TIME-SERIES DATA;

ADAPTIVE SYSTEMS; DATA PRIVACY; ESTIMATION; KNOWLEDGE MANAGEMENT; SAMPLING; TIME SERIES; TRAFFIC CONGESTION;

AGGREGATES;

EID: 84871058287     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2398595     Document Type: Conference Paper

References (16)

1. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50:174-188, 2001.
2. A. Blum, K. Ligett, and A. Roth. A learning theory approach to non-interactive database privacy. In STOC, 2008.
3. T.-H. H. Chan, E. Shi, and D. Song. Private and continual release of statistics. In S. Abramsky, C. Gavoille, C. Kirchner, F. M. auf der Heide, and P. G. Spirakis, editors, ICALP (2), volume 6199 of Lecture Notes in Computer Science, pages 405-417. Springer, 2010.
4. C. Dwork, F. Mcsherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In In Proceedings of the 3rd Theory of Cryptography Conference, pages 265-284. Springer, 2006.
5. C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum. Differential privacy under continual observation. In Proceedings of the 42nd ACM symposium on Theory of computing, STOC '10, pages 715-724, New York, NY, USA, 2010. ACM.
6. L. Fan and L. Xiong. Adaptively sharing time-series with differential privacy. CoRR, abs/1202.3461, 2012.
7. 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.
8. R. E. Kalman. A new approach to linear filtering and prediction problems. 1960.
9. M. King and M. King. Process Control: A Practical Approach. John Wiley & Sons, 2011.
10. C. Masreliez. Approximate non-gaussian filtering with linear state and observation relations. Automatic Control, IEEE Transactions on, 20(1):107-110, feb 1975.
11. F. McSherry. Privacy integrated queries: An extensible platform for privacy-preserving data analysis. In SIGMOD, 2009.
12. V. Rastogi and S. Nath. Differentially private aggregation of distributed time-series with transformation and encryption. In SIGMOD, 2010.
13. X. Xiao, G. Bender, M. Hay, and J. Gehrke. ireduct: differential privacy with reduced relative errors. In Proceedings of the 2011 international conference on Management of data, SIGMOD '11, pages 229-240, New York, NY, USA, 2011. ACM.
14. X. Xiao, G. Wang, and J. Gehrke. Differential privacy via wavelet transforms. Knowledge and Data Engineering, IEEE Transactions on, 23(8):1200-1214, aug. 2011.
15. Y. Xiao, L. Xiong, and C. Yuan. Differentially private data release through multidimensional partitioning. In W. Jonker and M. Petkovic, editors, Secure Data Management, volume 6358 of Lecture Notes in Computer Science, pages 150-168. Springer Berlin / Heidelberg, 2010.
16. J. Xu, Z. Zhang, X. Xiao, Y. Yang, and G. Yu. Differentially private histogram publication. In ICDE, 2012.