APHID: An architecture for private, high-performance integrated data mining

Future Generation Computer Systems

Volumn 26, Issue 7, 2010, Pages 891-904

  Secretan, Jimmy ^a   Georgiopoulos, Michael ^a   Koufakou, Anna ^a   Cardona, Kel ^b  

^a University of Central Florida   (United States)

^b UNIVERSITY OF PUERTO RICO   (Puerto Rico)

Author keywords

Data mining; Distributed architectures; Privacy

Indexed keywords

ALGORITHM DEVELOPMENT; DATA MINING APPLICATIONS; DATA MINING PROCESS; DISTRIBUTED ARCHITECTURES; GRID RESOURCE; INTEGRATED DATA MINING; PARALLEL COMPUTER; PRIVACY; PRIVACY-PRESERVING DATA MINING; SOFTWARE FRAMEWORKS; SYSTEM SUPPORTS;

ABSTRACTING; CLUSTERING ALGORITHMS; DATA PRIVACY; MINING; SOFTWARE ARCHITECTURE;

DATA MINING;

EID: 77955717757     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2010.02.017     Document Type: Conference Paper

References (86)

1. U. Fayyad, and P. Stolorz Data mining and kdd: promise and challenges Future Generation Computer Systems 13 23 1997 99 115 data Mining [Online] Available: http://www.sciencedirect.com/science/article/B6V06-3SP6192-2/2/ e5d424fc49af8d966cc437a78b669252
2. B. Schneier Applied Cryptography 2nd ed. 1995 John Wiley & Sons
3. J. Vaidya, and C. Clifton Privacy-preserving data mining: why, how, and when IEEE Security and Privacy 2 6 2004 19 27
4. W. Du, Z. Zhan, Building decision tree classifier on private data, in: IEEE International Conference on Data Mining Workshop on Privacy, Security, and Data Mining, vol. 14, Maebashi City, Japan, December 2002, pp. 18.
5. M. Kantarcoglu, J. Vaidya, Privacy preserving naive bayes classifier for horizontally partitioned data, in: IEEE ICDM Workshop on Privacy Preserving Data Mining, Melbourne, FL, November 2003, pp. 39.
6. A. Inan, Y. Saygyn, E. Savas, A.A. Hintoglu, A. Levi, Privacy preserving clustering on horizontally partitioned data, in: ICDEW '06: Proceedings of the 22nd International Conference on Data Engineering Workshops, ICDEW'06, IEEE Computer Society, Washington, DC, USA, 2006, p. 95.
7. J. Vaidya, C. Clifton, Privacy-preserving K-means clustering over vertically partitioned data, in: The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, August 2003 [Online] Available: http://www.cs.purdue.edu/homes/jsvaidya/pub-papers/vaidya-kmeans.pdf.
8. G. Jagannathan, and R.N. Wright Privacy-preserving distributed k-means clustering over arbitrarily partitioned data KDD'05: Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining 2005 ACM Press New York, NY, USA 593 599
9. H. Yu, X. Jiang, and J. Vaidya Privacy-preserving svm using nonlinear kernels on horizontally partitioned data SAC'06: Proceedings of the 2006 ACM Symposium on Applied Computing 2006 ACM Press New York, NY, USA 603 610
10. M. Kantarcioglu, C. Clifton, Privately computing a distributed k-nn classifier, in: 15th European Conference on Machine Learning, ECML and the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD, Pisa, Italy, September 2004.
11. Y. Huang, Z. Lu, H. Hu, Privacy preserving association rule mining with scalar product, in: Proceedings of IEEE International Conference on Natural Language Processing and Knowledge Engineering 2005, OctNov 2005, pp. 750755.
12. J. Secretan, M. Georgiopoulos, J. Castro, A privacy preserving probabilistic neural network for horizontally partitioned databases, Aug. 2007.
13. R.O. Sinnott From access and integration to mining of secure genomic data sets across the grid Future Generation Computer Systems 23 3 2007 447 456 [Online] Available: http://www.sciencedirect.com/science/article/B6V06-4KKWVH8- 1/2/ecc3466e26b3a8d08574823a9eea3bbe
14. Y. Lindell, and B. Pinkas Privacy preserving data mining Journal of Cryptology 15 3 2002
15. J. Vaidya, C. Clifton, Privacy preserving association rule mining in vertically partitioned data, in: Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 2002.
16. J. Vaidya, C. Clifton, Privacy preserving nave bayes classifier for vertically partitioned data, in: 2004 SIAM International Conference on Data Mining, Orlando, FL, May, 2004.
17. R. Wright, Z. Yang, Privacy-preserving bayesian network structure computation on distributed heterogeneous data, in: Proceedings of The Tenth ACM SIGKDD Conference, KDD'04, Seattle, WA, August 2004 [Online] Available: http://portal.acm.org/citation.cfm?id=1014145.
18. S.R.M. Oliveira, O.R. Zaane, Toward standardization in privacy-preserving data mining, in: Proc. of the 3nd Workshop on Data Mining Standards, DM-SSP 2004, in conjuction with KDD 2004, 2004, pp. 717.
19. R. Agrawal, and R. Srikant Privacy-preserving data mining Proc. of the ACM SIGMOD Conference on Management of Data 2000 ACM Press 439 450 [Online] Available: http://www.almaden.ibm.com/cs/people/srikant/papers/sigmod00.pdf
20. A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke, Privacy preserving mining of association rules, in: Proc. of 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, KDD, July 2002.
21. S. Agrawal, J.R. Haritsa, A framework for high-accuracy privacy-preserving mining, in: ICDE '05: Proceedings of the 21st International Conference on Data Engineering, ICDE'05, IEEE Computer Society, Washington, DC, USA, 2005, pp. 193204.
22. A. Yao, How to generate and exchange secrets, in: Proc. 27th Annual Symposium on Foundations of Computer Science, 1986, pp. 162167.
23. O. Goldreich, S. Micali, and A. Wigderson How to play any mental game STOC'87: Proceedings of the Nineteenth Annual ACM Conference on Theory of Computing 1987 ACM Press New York, NY, USA 218 229
24. P. Paillier Public-key cryptosystems based on composite degree residuosity classes Advances in CryptologyEUROCRYPT'99 J. Stern, Lecture Notes in Computer Science vol. 1592 1999 Springer-Verlag 223 238
25. B. Goethals, S. Laur, H. Lipmaa, and T. Mielikinen On private scalar product computation for privacy-preserving data mining Information Security and Cryptology ICISC 2004, 7th International Conference, Seoul, Korea, December 23, 2004, Revised Selected Papers C. Park, S. Chee, Lecture Notes in Computer Science vol. 3506 2004 Springer 104 120
26. G. Jagannathan, K. Pillaipakkamnatt, R. Wright, A new privacy-preserving distributed k-clustering algorithm, in: Proceedings of the 2006 SIAM International Conference on Data Mining, SDM, 2006.
27. Z. Yang, and R.N. Wright Improved privacy-preserving bayesian network parameter learning on vertically partitioned data ICDEW'05: Proceedings of the 21st International Conference on Data Engineering Workshops 2005 IEEE Computer Society Washington, DC, USA 1196
28. H. Yu, X. Jiang, J. Vaidya, Privacy-preserving svm using nonlinear kernels on horizontally partitioned data, in: Selected Areas in Cryptography, Dijon, France, 2006.
29. B. Pinkas Cryptographic techniques for privacy-preserving data mining SIGKDD Explorations Newsletter 4 2 2002 12 19
30. H. Yu, J. Vaidya, and X. Jiang Privacy-preserving svm classification on vertically partitioned data Proceedings of PAKDD'06 Lecture Notes in Computer Science vol. 3918 2006 Springer-Verlag 647 656
31. J. Zhan, L. Change, and S. Matwin Privacy preserving k-nearest neighbor classification International Journal of Network Security 1 1 2005
32. M. Kantarcioglu, and C. Clifton Privacy-preserving distributed mining of association rules on horizontally partitioned data IEEE Transactions on Knowledge and Data Engineering 16 9 2004 1026 1037
33. W. Du, M.J. Atallah, Privacy-preserving cooperative statistical analysis, in: 17th Annual Computer Security Applications Conference, ACSAC'01, December 2001, pp. 102110.
34. A. Karr, X. Lin, A. Sanil, and J. Reiter Secure regression on distributed databases Journal of Computational and Graphics Statistics 14 2 2005 263 279
35. J. Reiter, A. Karr, C. Kohnen, X. Lin, A. Sanil, Secure regression for vertically partitioned, partially overlapping data, in: Proceedings of the ASA, 2004.
36. J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, in: Proceedings of OSDI'04: Sixth Symposium on Operating System Design and Implementation, December 2004.
37. C.-T. Chu, S.K. Kim, Y.-A. Lin, Y. Yu, G. Bradski, A.Y. Ng, and K. Olukotun Map-reduce for machine learning on multicore Proceedings of NIPS vol. 19 2006
38. M. Isard, M. Budiu, Y. Yu, A. Birrell, D. Fetterly, Dryad: distributed data-parallel programs from sequential building blocks, in: Proc. European Conference on Computer Systems, EuroSys, Lisbon, Portugal, March, 2007, pp. 5972.
39. L. Amini, H. Andrade, R. Bhagwan, F. Eskesen, R. King, P. Selo, Y. Park, and C. Venkatramani Spc: a distributed, scalable platform for data mining DMSSP'06: Proceedings of the 4th International Workshop on Data Mining Standards, Services and Platforms 2006 ACM New York, NY, USA 27 37
40. S. Parthasarathy, and R. Subramonian Facilitating data mining on a network of workstations Advances in Distributed and Parallel Knowledge Discovery 2000 AAAI Press
41. R. Natarajan, R. Sion, and T. Phan A grid-based approach for enterprise-scale data mining Future Generation Computer Systems 23 1 2007 48 54 [Online] Available: http://www.sciencedirect.com/science/article/B6V06-4K48N6N- 2/2/f641180f944cae00649ab74eec6cf5ce
42. M. Klusch, S. Lodi, and G. Moro Issues of agent-based distributed data mining AAMAS'03: Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems 2003 ACM New York, NY, USA 1034 1035
43. A. Prodromidis, and P. Chan Meta-learning in distributed data mining systems: Issues and approaches H. Kargupta, P. Chan, Book on Advances of Distributed Data Mining 2000 AAAI press
44. H. Kargupta, B. Park, D. Hershberger, and E. Johnson Collective data mining: a new perspective toward distributed data mining H. Kargupta, P. Chan, Advances in Distributed and Parallel Knowledge Discovery 1999 MIT/AAAI Press
45. P. Luo, K. L, Z. Shi, and Q. He Distributed data mining in grid computing environments Future Generation Computer Systems 23 1 2007 84 91 [Online] Available: http://www.sciencedirect.com/science/article/B6V06-4K48N6N-1/2/ 44efc60e645c33323fd56424a6d59754
46. J. Luo, M. Wang, J. Hu, and Z. Shi Distributed data mining on agent grid: issues, platform and development toolkit Future Generation Computer Systems 23 1 2007 61 68 [Online] Available: http://www.sciencedirect.com/science/article/ B6V06-4KBDW91-2/2/17cfe0c7e1a97ab2a6725ed290c867ec
47. I. Foster, C. Kesselman, Globus: a metacomputing infrastructure toolkit, International Journal of Supercomputer Applications, 11 (2), 115128.
48. S. Bailey, R. Grossman, H. Sivakumar, and A. Turinsky Papyrus: a system for data mining over local and wide area clusters and super-clusters ACM/IEEE Conference on Supercomputing 1999 ACM Press Portland, OR
49. M. Cannataro, D. Talia, and P. Trunfio Distributed data mining on the grid Future Generation Computer Systems 18 8 2002 1101 1112 [Online] Available: http://www.sciencedirect.com/science/article/B6V06-46HJM1H-2/2/ f6c66d37ba86199e6b7d59e87b9e00de
50. M. Cannataro, A. Congiusta, A. Pugliese, D. Talia, and P. Trunfio Distributed data mining on grids: services, tools, and applications IEEE Transactions on Systems, Man and Cybernetics, Part B 34 6 2004 2451 2465
51. M. Cannataro, A. Congiusta, D. Talia, P. Trunfio, A data mining toolset for distributed high-performance platforms, in: W.I. Press (Ed.), Proceedings of Data Mining 2002, Bologna, Italy, 2002.
52. A. Congiusta, D. Talia, and P. Trunfio Distributed data mining services leveraging wsrf Future Generation Computer Systems 23 1 2007 34 41 [Online] Available: http://www.sciencedirect.com/science/article/B6V06-4K66DM3-4/2/ 029fd85f3935fb4600b983c1bf2897d5
53. M.S. Prez, A. Snchez, V. Robles, P. Herrero, and J.M. Pea Design and implementation of a data mining grid-aware architecture Future Generation Computer Systems 23 1 2007 42 47 [Online] Available: http://www.sciencedirect. com/science/article/B6V06-4K66DM3-1/2/fcc2786693eca9882ac19b89f908375d
54. I. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 2005 Morgan Kaufmann San Francisco
55. V. Stankovski, M. Swain, V. Kravtsov, T. Niessen, D. Wegener, J. Kindermann, and W. Dubitzky Grid-enabling data mining applications with datamininggrid: an architectural perspective Future Generation Computer Systems 2007
56. S. Datta, K. Bhaduri, C. Giannella, R. Wolff, and H. Kargupta Distributed data mining in peer-to-peer networks Internet Computing, IEEE 10 4 2006 18 26
57. J. Wang, C. Xu, H. Shen, Z. Wu, and Y. Pan Dhai: dynamic hierarchical agent-based infrastructure for supporting large-scale distributed information processing ICCNMC X. Lu, W. Zhao, Lecture Notes in Computer Science vol. 3619 2005 Springer 873 882
58. S. Krishnaswamy, A. Zaslavsky, S. Loke, An architecture to support distributed data mining services in e-commerce environments, in: Second International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems, WECWIS, 2000, pp. 239246.
59. G. Tsoumakas, N. Bassiliades, I. Vlahavas, A knowledge-based web information system for the fusion of distributed classifiers. IDEA Group, 2004, pp. 271308.
60. R. Olejnik, T.-F. Fortis, and B. Toursel Webservices oriented data mining in knowledge architecture Future Generation Computer Systems 25 4 2009 436 443 [Online] Available: http://www.sciencedirect.com/science/article/B6V06-4TMJ3SJ- 2/2/5ad1a4cd59160b1ad04312a1971279ba
61. V. Fiolet, R. Olejnik, G. Lefait, B. Toursel, Optimal grid exploitation algorithms for data mining, pp. 246252, July 2006.
62. A. Kumar, M. Kantardzic, P. Ramaswamy, P. Sadeghian, An extensible service oriented distributed data mining framework, in: Proc. 2004 International Conference on Machine Learning and Applications, December, 2004, pp. 256263.
63. W. Ahmad, A. Khokhar, Triumf: a trusted middleware for fault-tolerant secure collaborative computing, University of Illinois at Chicago Tech Report, Tech. Rep. TR-MSL0786, August 2006.
64. Jxta technology, 2008. http://www.sun.com/software/jxta/.
65. J. Wang, C. Xu, H. Shen, Y. Pan, Hierarchical infrastructure for large-scale distributed privacy-preserving data mining, in: Proc. of the 5th International Conference on Computer Science, May 2005, pp. 10201023.
66. W. Ahmad, A. Khokhar, Towards secure and privacy preserving data mining over computational grids, in: Proceedings of the NSF International Workshop on Frontiers of Information Technology, December 2003.
67. D. Malkhi, N. Nisan, B. Pinkas, Y. Sella, Fairplay a secure two-party computation system, in: Proc. of the USENIX Security Symposium, 2004, pp. 287302.
68. J.D. Nielsen, and M.I. Schwartzbach A domain-specific programming language for secure multiparty computation PLAS'07: Proceedings of the 2007 Workshop on Programming Languages and Analysis for Security 2007 ACM New York, NY, USA 21 30
69. M.Z. Ashrafi, D. Taniar, K. Smith, A data mining architecture for distributed environments, in: Second International Workshop on Innovative Internet Computing Systems, June 2002, pp. 2738.
70. J.M. P na, E. Menasalvas, Towards flexibility in a distributed data mining framework, in: Proc. DMKD Workshop, 2001.
71. Welcome to hadoop! http://lucene.apache.org/hadoop/, 2007.
72. W. Du, G. Agrawal, Developing distributed data mining implementations for a grid environment, in: Proc. 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, May, 2002.
73. R. Jin, G. Agrawal, A middleware for developing parallel data mining applications, in: Proceedings of The First SIAM Conference on Data Mining, 2001.
74. Mpi forum, 2008.
75. T. Mielikinen, On inverse frequent set mining, 2003.
76. C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M.Y. Zhu Tools for privacy preserving distributed data mining SIGKDD Explorations 4 2 2003 28 34
77. H. Yu, J. Vaidya, X. Jiang, Privacy-preserving svm classification on vertically partitioned data, in: Pan-Asia Conference on Knowledge Discover and Data Mining, PAKDD, Singapore, 2006, pp. 647656.
78. M. Naor, and B. Pinkas Oblivious transfer and polynomial evaluation STOC'99: Proceedings of the Thirty-first Annual ACM Symposium on Theory of Computing 1999 ACM Press New York, NY, USA 245 254
79. A.F. Karr, X. Lin, J.P. Reiter, A.P. Sanil, Privacy preserving analysis of vertically partitioned data using secure matrix products, National Institute of Statistical Sciences, Tech. Rep., 2004.
80. W. Du, Y. Han, S. Chen, Privacy-preserving multivariate statistical analysis: linear regression and classification, in: Proceedings of the Fourth SIAM International Conference on Data Mining, 2004, pp. 222233.
81. T. Mitchell Machine Learning 1st ed. 1997 McGraw-Hill Science/Engineering/Math
82. P. Domingos, and M. Pazzani On the optimality of the simple bayesian classifier under zero-one loss Machine Learning 29 1997 103 137
83. D. Cristofor, and D. Simovici Finding median partitions using information-theoretical algorithms Journal of Universal Computer Science 2002 153 172 [Online] Available: http://www.cs.umb.edu/~dana/GAClust/index.html
84. K. Chiu, M. Govindaraju, and R. Bramley Investigating the limits of soap performance for scientific computing HPDC'02: Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing 2002 IEEE Computer Society Washington, DC, USA 246
85. R. Pike, S. Dorward, R. Griesemer, and S. Quinlan Interpreting the data: parallel analysis with sawzall Scientific Programming Journal 13 4 2005 227 298
86. Apache pig, http://incubator.apache.org/pig/, 2008.