By Guang-Zhong Yang (auth.), Thanaruk Theeramunkong, Boonserm Kijsirikul, Nick Cercone, Tu-Bao Ho (eds.)
This e-book constitutes the refereed lawsuits of the thirteenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2009, held in Bangkok, Thailand, in April 2009.
The 39 revised complete papers and seventy three revised brief papers awarded including three keynote talks have been rigorously reviewed and chosen from 338 submissions. The papers current new principles, unique study effects, and functional improvement stories from all KDD-related parts together with info mining, facts warehousing, laptop studying, databases, facts, wisdom acquisition, automated clinical discovery, information visualization, causal induction, and knowledge-based systems.
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Additional info for Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings
2: P1 converts its vector X to the position set XS 3: Each party Pi converts its local vector Yi to the position set Y Si based on the global encoding scheme 4: for i = 1 . . n do 5: P1 encrypts the position set XS with its key Epk1 to get the encrypted vector EXS 6: end for 7: for j = 1 . . k do 8: Each Party Pj encrypts its local position set Y Sj with its key Epkj to get the encrypted position set EY Sj 9: end for 10: Each party passes its encrypted position set to the next party for encryption with its key until all sets are encrypted by all parties 11: At Pk : EY S ← φ 12: for j = k .
This makes sense, since these are the only nodes involved in any edges with intra-edges. These are our only sources of cross-graph information. As we have explained above, combining our cross-graph path information with the local paths in each subgraph will help us to discover the path between any pair of nodes residing in different subgraphs. Assuming that the inter-nodes is a fraction of total nodes, choosing inter-nodes as representatives would easily reduce the required secure computations.
In , Liu and Terzi investigated how to modify a graph via a set of edge addition (or deletion) operations in order to construct a new k-degree anonymous graph, in which every node has the same degree with at least k − 1 other nodes. In , Zhou and Pei anonymized the graph by generalizing node labels and inserting edges until each neighborhood is indistinguishable to at least k − 1 others. In [2, 10], authors applied a structural anonymization approach called edge generalization that consists of collapsing clusters together with their component nodes’ structure, rather than add or delete edges from the social network dataset.
Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings by Guang-Zhong Yang (auth.), Thanaruk Theeramunkong, Boonserm Kijsirikul, Nick Cercone, Tu-Bao Ho (eds.)