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Random projection-based multiplicative data perturbation for privacy preserving distributed data mining
Abstract
This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores independent component analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy-preserving data mining applications.
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Citations
Cites
Capacity of Multi‐antenna Gaussian Channels
Telatar, E. (1999) · European Transactions on Telecommunications · doi:10.1002/ett.4460100604 · Cited by 11,234
Independent component analysis: algorithms and applications
Hyvärinen, A., & Oja, E. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,705
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,205
Independent Component Analysis
(2004) · Independent Component Analysis · doi:10.7551/mitpress/3717.003.0014 · Cited by 7,903
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188
How to generate and exchange secrets
Yao, A. C.-C. (1986) · 27th Annual Symposium on Foundations of Computer Science (sfcs 1986) · doi:10.1109/sfcs.1986.25 · Cited by 3,733
Goldreich, O., Micali, S., & Wigderson, A. (1987) · Proceedings of the nineteenth annual ACM conference on Theory of computing - STOC '87 · doi:10.1145/28395.28420 · Cited by 3,590
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L. (1965) · Journal of the American Statistical Association · doi:10.2307/2283137 · Cited by 3,263
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480775 · Cited by 3,255
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 2,961
Protocols for secure computations
Yao, A. C.-C. (1982) · doi:10.1109/sfcs.1982.88 · Cited by 2,661
Extensions of Lipschitz mappings into a Hilbert space
Johnson, W. B., & Lindenstrauss, J. (1984) · Contemporary Mathematics · doi:10.1090/conm/026/737400 · Cited by 2,523
Goldreich, O. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546891 · Cited by 2,181
Goldreich, O. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511721656 · Cited by 1,805
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335438 · Cited by 1,719
Applied Multivariate Statistical Analysis
Härdle, W. K., & Simar, L. (2015) · Springer eBooks · doi:10.1007/978-3-662-45171-7 · Cited by 1,634
Foundations of Cryptography: Volume 2, Basic Applications
Goldreich, O. (2004) · Cited by 1,592
Gupta, A. K., & Nagar, D. K. (2018) · Chapman & Hall/CRC eBooks · doi:10.1201/9780203749289 · Cited by 1,295
Learning Overcomplete Representations
Lewicki, M. S., & Sejnowski, T. J. (2000) · Neural Computation · doi:10.1162/089976600300015826 · Cited by 1,129
Privacy Preserving Data Mining
Lindell, Y., & Pinkas, B. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44598-6_3 · Cited by 1,077
On the design and quantification of privacy preserving data mining algorithms
Agrawal, D., & Aggarwal, C. C. (2001) · Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/375551.375602 · Cited by 1,006
Privacy preserving association rule mining in vertically partitioned data
Vaidya, J., & Clifton, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775142 · Cited by 1,001
Security-control methods for statistical databases: a comparative study
Adam, N. R., & Worthmann, J. C. (1989) · ACM Computing Surveys · doi:10.1145/76894.76895 · Cited by 969
Tools for privacy preserving distributed data mining
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/772862.772867 · Cited by 890
State-of-the-art in privacy preserving data mining
Verykios, V. S., Bertino, E., Fovino, I. N., Provenza, L. P., Saygin, Y., & Theodoridis, Y. (2004) · ACM SIGMOD Record · doi:10.1145/974121.974131 · Cited by 816
Limiting privacy breaches in privacy preserving data mining
Evfimievski, A., Gehrke, J., & Srikant, R. (2003) · Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/773153.773174 · Cited by 815
Privacy preserving mining of association rules
Evfimievski, A., Srikant, R., Agrawal, R., & Gehrke, J. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775080 · Cited by 768
Underdetermined blind source separation using sparse representations
Bofill, P., & Zibulevsky, M. (2001) · Signal Processing · doi:10.1016/s0165-1684(01)00120-7 · Cited by 715
Oblivious transfer and polynomial evaluation
Naor, M., & Pinkas, B. (1999) · Proceedings of the thirty-first annual ACM symposium on Theory of Computing · doi:10.1145/301250.301312 · Cited by 629
Privacy-preserving k -means clustering over vertically partitioned data
Vaidya, J., & Clifton, C. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956776 · Cited by 626
On the privacy preserving properties of random data perturbation techniques
Kargupta, H., Datta, S., Wang, Q., & Sivakumar, K. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250908 · Cited by 618
Self-citationOne-Way Accumulators: A Decentralized Alternative to Digital Signatures
Benaloh, J., & de Mare, M. (1994) · Lecture Notes in Computer Science · doi:10.1007/3-540-48285-7_24 · Cited by 572
Verykios, V. S., Elmagarmid, A. K., Bertino, E., Saygin, Y., & Dasseni, E. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.1269668 · Cited by 518
Maintaining Data Privacy in Association Rule Mining
Rizvi, S. J., & Haritsa, J. R. (2002) · VLDB '02: Proceedings of the 28th International Conference on Very Large Databases · doi:10.1016/b978-155860869-6/50066-4 · Cited by 485
Efficient Receipt-Free Voting Based on Homomorphic Encryption
Hirt, M., & Sako, K. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45539-6_38 · Cited by 483
Secure multi-party computation problems and their applications: a review and open problems
Du, W., & Atallah, M. J. (2001) · Proceedings of the 2001 workshop on New security paradigms · doi:10.1145/508171.508174 · Cited by 453
Cryptographic techniques for privacy-preserving data mining
Pinkas, B. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/772862.772865 · Cited by 430
Disclosure limitation of sensitive rules
Atallah, M. J., Bertino, E., Elmagarmid, A. K., Ibrahim, M., & Verykios, V. S. (2003) · doi:10.1109/kdex.1999.836532 · Cited by 426
Dimensionality reduction by random mapping: fast similarity computation for clustering
Kaski, S. (2002) · 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227) · doi:10.1109/ijcnn.1998.682302 · Cited by 388
Building decision tree classifier on private data
Du, W. K., & Zhan, Z. (2002) · Syracuse University Libraries (Syracuse University) · Cited by 382
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
Du, W., Han, Y. S., & Chen, S. (2004) · Proceedings of the 2004 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.21 · Cited by 366
Data-swapping: A technique for disclosure control
Dalenius, T., & Reiss, S. P. (1982) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(82)90058-1 · Cited by 349
Two algorithms for nearest-neighbor search in high dimensions
Kleinberg, J. M. (1997) · Proceedings of the twenty-ninth annual ACM symposium on Theory of computing - STOC '97 · doi:10.1145/258533.258653 · Cited by 348
Using unknowns to prevent discovery of association rules
Saygin, Y., Verykios, V. S., & Clifton, C. (2001) · ACM SIGMOD Record · doi:10.1145/604264.604271 · Cited by 338
An algorithmic theory of learning: Robust concepts and random projection
Arriaga, R. I., & Vempala, S. (2006) · Machine Learning · doi:10.1007/s10994-006-6265-7 · Cited by 316
Experiments with Random Projection
Dasgupta, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3849 · Cited by 306
Privacy Preserving Clustering by Data Transformation
Oliveira, S. R. D. M., & Zai͏̈ane, O. R. (2003) · Journal of Information and Data Management · doi:10.5753/jidm.2010.940 · Cited by 294
JAM: java agents for meta-learning over distributed databases
Stolfo, S. J., Prodromidis, A. L., Tselepis, S., Lee, W., Fan, D. W., & Chan, P. K. (1997) · Cited by 285
Privacy-preserving cooperative statistical analysis
Du, W., & Atallah, M. J. (2005) · Seventeenth Annual Computer Security Applications Conference · doi:10.1109/acsac.2001.991526 · Cited by 279
Toward Privacy in Public Databases
Chawla, S., Dwork, C., McSherry, F., Smith, A., & Wee, H. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30576-7_20 · Cited by 264
Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data
Vaidya, J., & Clifton, C. (2004) · Proceedings of the 2004 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.59 · Cited by 262
Privacy preserving association rule mining
Saygin, Y., Verykios, V. S., & Elmagarmid, A. K. (2003) · Proceedings Twelfth International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems RIDE-2EC 2002 · doi:10.1109/ride.2002.995109 · Cited by 241
Secure Multi-party Computational Geometry
Atallah, M. J., & Du, W. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-44634-6_16 · Cited by 234
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Wright, R., & Yang, Z. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014145 · Cited by 219
A data distortion by probability distribution
Liew, C. K., Choi, U. J., & Liew, C. J. (1985) · ACM Transactions on Database Systems · doi:10.1145/3979.4017 · Cited by 202
Privacy-preserving distributed clustering using generative models
Merugu, S., & Ghosh, J. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250922 · Cited by 202
Privacy preserving mining of association rules
Evfimievski, A., Srikant, R., Agrawal, R., & Gehrke, J. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02 · doi:10.1145/775079.775080 · Cited by 198
A Framework for High-Accuracy Privacy-Preserving Mining
Agrawal, S., & Haritsa, J. R. (2005) · 21st International Conference on Data Engineering (ICDE'05) · doi:10.1109/icde.2005.8 · Cited by 177
Privacy-preserving cooperative scientific computations
Du, W., & Atallah, M. J. (2005) · Proceedings. 14th IEEE Computer Security Foundations Workshop, 2001. · doi:10.1109/csfw.2001.930152 · Cited by 166
Distributed Clustering Using Collective Principal Component Analysis
Kargupta, H., Huang, W., Sivakumar, K., & Johnson, E. (2001) · Knowledge and Information Systems · doi:10.1007/pl00011677 · Cited by 161
Self-citationData Swapping: Variations on a Theme by Dalenius and Reiss
Fienberg, S. E., & McIntyre, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25955-8_2 · Cited by 158
Privacy-preserving distributed mining of association rules on horizontally partitioned data
Kantarcıoğlu, M., & Clifton, C. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.45 · Cited by 149
Privacy-preserving clustering with distributed EM mixture modeling
Lin, X. S., Clifton, C., & Zhu, M. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-004-0148-7 · Cited by 149
Protocols for Secure Remote Database Access with Approximate Matching
Du, W. K., & Atallah, M. J. (2001) · Advances in information security · doi:10.1007/978-1-4615-1467-1_6 · Cited by 140
Multiplicative Noise for Masking Continuous Data
Kim, J. J., & Winkler, W. E. (2001) · Cited by 133
An algorithmic theory of learning: robust concepts and random projection
Arriaga, R. I., & Vempala, S. (2003) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sffcs.1999.814637 · Cited by 96
Tsoumakas, G. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-010-3.ch110 · Cited by 60
An Analytic Approach to Statistical Databases
Lefons, E., SILVESTRI, A., & Tangorra, F. (1983) · Cited by 54
Improved Error Bounds for Underdetermined System Solvers
Demmel, J. W., & Higham, N. J. (1993) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/0614001 · Cited by 49
Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data
Giannella, C. R., Liu, K., Olsen, T., & Kargupta, H. (2005) · Proceedings · doi:10.1109/icdm.2004.10114 · Cited by 36
Self-citationPrivacy-Sensitive Bayesian Network Parameter Learning
meng, D., Sivakumar, K., & Kargupta, H. (2005) · Proceedings · doi:10.1109/icdm.2004.10076 · Cited by 34
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Al-Rubaie, M., & Chang, J. M. (2019) · IEEE Security & Privacy · doi:10.1109/msec.2018.2888775 · Cited by 444
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Metadata
- Authors
- Liu, K., Kargupta, H., & Ryan, J.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2006-01
- Volume
- 18
- Issue
- 1
- Pages
- 92-106
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2160553465
- Cited by
- 554
- References
- 72
- FWCI
- 35.59
- Type
- journal article
- Language
- en
- Open access
- closed
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