Right-protected data publishing with hierarchical clustering preservation
Pages 654 - 663
Abstract
The emergence of cloud-based storage services is opening up new avenues in data exchange and data dissemination. This has amplified the interest in right-protection mechanisms for establishing ownership in case of data leakage. Current right-protection technologies, however, rarely provide strong guarantees on the dataset utility after the protection process. This work presents techniques that explicitly address this shortcoming and provably preserve the outcome of certain mining operations. In particular, we take special care to guarantee that the outcome of hierarchical clustering operations remains the same before and after right protection. We encode data ownership using watermarking principles. In the process, we derive fundamental bounds on the distortion incurred by the watermarking. We leverage our theoretical analysis to design fast algorithms for right protection without exhaustively searching the vast design space.
References
[1]
C. C. Aggarwal and P. S. Yu. A condensation approach to privacy preserving data mining. In International Conference on Extending Database Technology, pages 183--199, 2004.
[2]
R. Agrawal and J. Kiernan. Watermarking relational databases. In 28th International Conference on Very Large Databases, pages 155--166, 2002.
[3]
P. Bassia and I. Pitas. Robust audio watermarking in the time domain. In 9th European Signal Processing Conference, pages 25--28, 1998.
[4]
K. Chen and L. Liu. Privacy preserving data classification with rotation perturbation. In International Conference on Data Mining, pages 589--592, 2005.
[5]
E. Cope and G. Antonini. Observed correlations and dependencies among operational losses in the ORX consortium database. In Journal of Operational Risk, 2008.
[6]
I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon. Secure spread spectrum watermarking for multimedia. IEEE Transactions on Image Processing, 6(12):1673--1687, 1997.
[7]
D. Defays. An efficient algorithm for a complete link method. Comput. J., 20(4):364--366, 1977.
[8]
O. Devillers and M. J. Golin. Incremental algorithms for finding the convex hulls of circles and the lower envelopes of parabolas. Inf. Process. Lett., 56(3):157--164, 1995.
[9]
I. M. Faniel and A. Zimmerman. Beyond the Data Deluge: A Research Agenda for Large-Scale Data Sharing and Reuse. In Proc. of 6th International Digital Curation Conference, 2010.
[10]
R. Geambasu, S. D. Gribble, and H. M. Levy. CloudViews: Communal Data Sharing in Public Clouds. In Proc. of HotCloud, 2009.
[11]
G. Jagannathan, K. Pillaipakkamnatt, and R. N. Wright. A new privacy-preserving distributed k-clustering algorithm. In SIAM International Conference on Data Mining, 2006.
[12]
H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the privacy preserving properties of random data perturbation techniques. In 3rd IEEE International Conference on Data Mining, pages 99--106, 2003.
[13]
L. Liu, M. Kantarcioglu, and B. Thuraisingham. The applicability of the perturbation model-based privacy preserving data mining for real-world data. In 6th IEEE International Conference on Data Mining, pages 507--512, 2006.
[14]
C. Lucchese, M. Vlachos, D. Rajan, and P. S. Yu. Rights protection of trajectory datasets with nearest-neighbor preservation. The VLDB Journal, 19(4):531--556, 2010.
[15]
W. Ludwig and H.-P. Klenk. Overview: A phylogenetic backbone and taxonomic framework for prokaryotic systematics. In Manual of Systematic Bacteriology, pages 49--65, 2001.
[16]
M. C. Mont, I. Matteucci, M. Petrocchi, and M. L. Sbodio. Enabling Data Sharing in the Cloud. In HP Laboratories, Tech Report HPL-2012--22, 2012.
[17]
P. Moulin, M. E. Mihcak, and G.-I. Lin. An information-theoretic model for image watermarking and data hiding. In IEEE International Conference on Image Processing, pages 667--670, 2000.
[18]
X. Niu, C. Shao, and X. Wang. A survey of digital vector map watermarking. International Journal of Innovative Computing, Information and Control, 2(6):1301--1316, 2006.
[19]
S. Oliveira and O. Zaiane. Privacy preserving clustering by data transformation. In 18th Brazilian Symposium on Databases, pages 304--318, 2003.
[20]
M. Piorkowski, N. Sarafijanovoc-Djukic, and M. Grossglauser. A Parsimonious Model of Mobile Partitioned Networks with Clustering. In The First International Conference on COMmunication Systems and NETworkS (COMSNETS), January 2009.
[21]
R. Sibson. Slink: An optimally efficient algorithm for the single-link cluster method. Comput. J., 16(1):30--34, 1973.
[22]
J. V. Sickle. Using Mean Similarity Dendrograms to Evaluate Classifications. In Journal of Agricultural, Biological and Environmental Statistics, pages 370--384, 2001.
[23]
D. Simitopoulos, S. A. Tsaftaris, N. V. Boulgouris, and M. G. Strintzis. Compressed-domain video watermarking of MPEG streams. In IEEE International Conference on Multimedia and Expo, volume 1, pages 569--572, 2002.
[24]
R. Sion, M. Atallah, and S. Prabhakar. Rights protection for relational data. IEEE Transactions on Knowledge and Data Engineering, 16(12):1509--1525, 2004.
[25]
R. Sion, M. J. Atallah, and S. Prabhakar. Rights Protection for Discrete Numeric Streams. IEEE Transactions on Knowledge and Data Engineering, 18(5):699--714, 2006.
[26]
M. D. Swanson, B. Zhu, A. H. Tewfik, and L. Boney. Robust audio watermarking using perceptual masking. Signal Processing, 66(3):337--355, 1998.
[27]
A. Z. V. Zabkar. Application of End-Users Market Segmentation using Statistical Methods. In Advances in Methodology and Statistics (19), 2003.
[28]
J. Vaidya and C. Clifton. Privacy-preserving k-means clustering over vertically partitioned data. In 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 206--215, 2003.
[29]
H. Yu, X. Jiang, and J. Vaidya. Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data. In ACM Symposium on Applied Computing, pages 603--610, 2006.
[30]
H. Yu, J. Vaidya, and X. Jiang. Privacy-preserving SVM classification on vertically partitioned data. In 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 647--656, 2006.
[31]
J. Yuan, Y. Zheng, X. Xie, and G. Sun. Driving with knowledge from the physical world. In KDD, pages 316--324, 2011.
[32]
J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-drive: driving directions based on taxi trajectories. In GIS, pages 99--108, 2010.
[33]
W. Zhu, Z. Xiong, and Y.-Q. Zhang. Multiresolution watermarking for images and video. IEEE Transactions on Circuits and Systems for Video Technology, 9(4):545--550, 1999.
Index Terms
- Right-protected data publishing with hierarchical clustering preservation
Recommendations
On Data Publishing with Clustering Preservation
TKDD Special Issue (SIGKDD'13)The emergence of cloud-based storage services is opening up new avenues in data exchange and data dissemination. This has amplified the interest in right-protection mechanisms to establish ownership in the event of data leakage. Current right-protection ...
A novel reversible data hiding scheme based on AMBTC compression technique
Data hiding encompasses a wide range of applications for embedding messages in content. However, hiding data inevitably destroys the host image, even though the distortion is imperceptible. To enhance the hiding capacity and maintain the quality of the ...
Lossless data embedding: new paradigm in digital watermarking
One common drawback of virtually all current data embedding methods is the fact that the original image is inevitably distorted due to data embedding itself. This distortion typically cannot be removed completely due to quantization, bit-replacement, or ...
Comments
Information & Contributors
Information
Published In
October 2012
2840 pages
ISBN:9781450311564
DOI:10.1145/2396761
- General Chair:
- Xuewen Chen,
- Program Chairs:
- Guy Lebanon,
- Haixun Wang,
- Mohammed J. Zaki
Copyright © 2012 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 October 2012
Check for updates
Author Tag
Qualifiers
- Research-article
Conference
CIKM'12: 21st ACM International Conference on Information and Knowledge Management
October 29 - November 2, 2012
Hawaii, Maui, USA
Acceptance Rates
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 254Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 22 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in