Electrical computers: arithmetic processing and calculating – Electrical digital calculating computer – Particular function performed
Reexamination Certificate
2007-09-14
2011-12-27
Malzahn, David H (Department: 2193)
Electrical computers: arithmetic processing and calculating
Electrical digital calculating computer
Particular function performed
Reexamination Certificate
active
08086655
ABSTRACT:
Techniques for perturbing an evolving data stream are provided. The evolving data stream is received. An online linear transformation is applied to received values of the evolving data stream generating a plurality of transform coefficients. A plurality of significant transform coefficients are selected from the plurality of transform coefficients. Noise is embedded into each of the plurality of significant transform coefficients, thereby perturbing the evolving data stream. A total noise variance does not exceed a defined noise variance threshold.
REFERENCES:
patent: 6332030 (2001-12-01), Manjunath et al.
patent: 6681029 (2004-01-01), Rhoads
patent: 7127079 (2006-10-01), Keating et al.
patent: 2004/0068399 (2004-04-01), Ding
patent: 2007/0239444 (2007-10-01), Ma
patent: 2008/0010671 (2008-01-01), Mates
patent: 2008/0209568 (2008-08-01), Chang et al.
R. Agrawal et al., “Privacy-Preserving Data Mining,” SIGMOD, 2000, 12 pages.
Davis Automotive, “CarChip,” http://www.davisnet.com/drive/products/carchip.aso, 2 pages.
W.P. Schiefele et al., “SensorMiner: Tool Kit for Anomaly Detection in Physical Time Series,” ACM, Conference, 2004, pp. 1-10.
Y. Zhu et al., “StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time,” Proceedings of the 28th VLDB Conference, 2002, 12 pages.
D. Agrawal et al., “On the Design and Quantification of Privacy Preserving Data Mining Algorithms,” PODS, 2001, 9 pages.
W. Du et al., “Using Randomized Response Techniques for Privacy-Preserving Data Mining,” SIGKDD, Aug. 2003, pp. 1-6.
H. Kargupta et al., “On the Privacy Preserving Properties of Random Data Perturbation Techniques,” ICDM, 2003, 8 pages.
Z. Huang et al., “Deriving Private Information from Randomized Data,” SIGMOD, Jun. 2005, pp. 37-48.
K. Liu et al., “Random Projection-Based Multiplicative Data Perturbation for Privacy Preserving Distributed Data Mining,” IEEE Transactions on Knowledge and Data Engineering, Jan. 2006, pp. 92-106, vol. 18, No. 1.
K. Chen et al., “Privacy Preserving Data Classification with Rotation Perturbation,” ICDM, 2005, pp. 1-4.
L. Sweeney, “k-Anonymity: A Model for Protecting Privacy,” International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 2002, pp. 1-14, vol. 10, No. 5.
C.C. Aggarwal et al., “A Condensation Approach to Privacy Preserving Data Mining,” EDBT, 2004, 18 pages.
E. Bertino et al., “Privacy and Ownership Preserving of Outsourced Medical Data,” ICDE, 2005, 12 pages.
A. Machanavajjhala et al., “I-Diversity: Privacy Beyond k-Anonymity,” ICDE, 2006, pp. 1-12.
D.L. Donoho et al., “Uncertainty Principles and Signal Recovery,” Society for Industrial and Applied Mathematics, Jun. 1989, pp. 906-931, vol. 49, No. 3.
C.C. Aggarwal, “On k-Anonymity and the Curse of Dimensionality,” Proceedings of the 31st VLDB Conference, 2005, pp. 901-909.
D.L. Donoho, “Wavelet Shrinkage and W.V.D.: A 10-Minute Tour,” International Conference on Wavelets and Applications, Jun. 1992, pp. 1-12.
T. Li et al., “A Survey on Wavelet Applications in Data Mining,” SIGKDD Explorations, 2002, pp. 49-68, vol. 4, No. 2.
D.L. Donoho et al., “Adapting to Unknown Smoothness via Wavelet Shrinkage,” J. Am. Stat. Soc., Jul. 1994, pp. 1-28, vol. 90.
D.L. Donoho, “De-Noising by Soft-Thresholding,” IEEE TOIT, 1995, pp. 1-37, vol. 41, No. 3.
F. Li et al., “Hiding in the Crowd: Privacy Preservation on Evolving Streams through Correlation Tracking,” ICDE, 2007, 10 pages.
A. Evfimievski et al., “Limiting Privacy Breaches in Privacy Preserving Data Mining,” PODS, Jun. 2003, 12 pages.
D. Kifer et al., “Injecting Utility into Anonymized Datasets,” SIGMOD, Jun. 2006, 12 pages.
D.L. Donoho, “Compressed Sensing,” IEEE TOIT, Sep. 2004, pp. 1-34, vol. 52, No. 4.
Y. Zhu et al., “Efficient Elastic Burst Detection in Data Streams,” SIGKDD, Aug. 2003, 10 pages.
M. Vlachos et al., “Structural Periodic Measures for Time-Series Data,” Data Mining and Knowledge Discovery, Feb. 2006, pp. 1-28, vol. 12, No. 1.
E. Keogh, “Time Series Data Mining Archive,” University of California, Riverside, Department of Computer Science & Engineering, http://www.cs.ucr.edu/˜eamonn/TSDMA/main.php, 2002, 2 pages.
X. Xiao et al., “Personalized Privacy Preservation,” SIGMOD, Jun. 2006, 12 pages.
Papadimitriou Spyridon
Yu Philip Shi-Lung
International Business Machines - Corporation
Malzahn David H
Ryan & Mason & Lewis, LLP
Stock William J.
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