Paper Info
Title
A fixed structure learning automaton micro-aggregation technique for secure statistical databases
Abstract
We consider the problem of securing statistical databases and, more specifically, the micro-aggregation technique (MAT), which coalesces the individual records in the micro-data file into groups or classes, and on being queried, reports, for the all individual values, the aggregated means of the corresponding group. This problem is known to be NP-hard and has been tackled using many heuristic solutions. In this paper we present the first reported Learning Automaton (LA) based solution to the MAT. The LA modifies a fixed-structure solution to the Equi-Partitioning Problem (EPP) to solve the micro-aggregation problem. The scheme has been implemented, rigorously tested and evaluated for different real and simulated data sets. The results clearly demonstrate the applicability of LA to the micro-aggregation problem, and to yield a solution that obtains a lower information loss when compared to the best available heuristic methods for micro-aggregation.
Year
DOI
Venue
2006
10.1007/11930242_11
Privacy in Statistical Databases
Keywords
Field
DocType
automaton micro-aggregation technique,fixed-structure solution,micro-aggregation problem,equi-partitioning problem,heuristic solution,micro-aggregation technique,fixed structure,learning automaton,secure statistical databases,individual value,individual record,available heuristic method,aggregated mean,micro data
Learning cycle,Information loss,Heuristic,Data set,Computer science,Automaton,Structure learning,Algorithm,Database
Conference
Volume
ISSN
ISBN
4302
0302-9743
3-540-49330-1
Citations 
PageRank 
References 
6
0.45
11
Authors
2
Name
Order
Citations
PageRank
Ebaa Fayyoumi1506.77
B. John Oommen21255222.20