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Approximation Algorithms for Min-Max Generalization Problems

Content Provider	ACM Digital Library
Author	Raskhodnikova, Sofya Berman, Piotr
Copyright Year	2014
Abstract	We provide improved approximation algorithms for the min-max generalization problems considered by Du, Eppstein, Goodrich, and Lueker [Du et al. 2009]. Generalization is widely used in privacy-preserving data mining and can also be viewed as a natural way of compressing a dataset. In min-max generalization problems, the input consists of data items with weights and a lower bound $w_{lb},$ and the goal is to partition individual items into groups of weight at least $w_{lb}$ while minimizing the maximum weight of a group. The rules of legal partitioning are specific to a problem. Du et al. consider several problems in this vein: (1) partitioning a graph into connected subgraphs, (2) partitioning unstructured data into arbitrary classes, and (3) partitioning a two-dimensional array into contiguous rectangles (subarrays) that satisfy these weight requirements. We significantly improve approximation ratios for all the problems considered by Du et al. and provide additional motivation for these problems. Moreover, for the first problem, whereas Du et al. give approximation algorithms for specific graph families, namely, 3-connected and 4-connected planar graphs, no approximation algorithm that works for all graphs was known prior to this work.
Starting Page	1
Ending Page	23
Page Count	23
File Format	PDF
ISSN	15496325
e-ISSN	15496333
DOI	10.1145/2636920
Volume Number	11
Issue Number	1
Journal	ACM Transactions on Algorithms (TALG)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	$\textit{k}-anonymity$ Generalization problems Bin covering Rectangle tiling
Content Type	Text
Resource Type	Article
Subject	Mathematics

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