Name
Title | ||
|---|---|---|
Achieving microaggregation for secure statistical databases using fixed-structure partitioning-based learning automata. |
Abstract | ||
|---|---|---|
We consider the microaggregation problem (MAP) that involves partitioning a set of individual records in a microdata file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the microdata file, is known to be NP-hard and has been tackled using many heuristic solutions. In this paper, we present the first reported fixed-structure-stochastic-automata-based solution to this problem. The newly proposed method leads to a lower value of the information loss (IL), obtains a better tradeoff between the IL and the disclosure risk (DR) when compared with state-of-the-art methods, and leads to a superior value of the scoring index, which is a criterion involving a combination of the IL and the DR. The scheme has been implemented, tested, and evaluated for different real-life and simulated data sets. The results clearly demonstrate the applicability of learning automata to the MAP and its ability to yield a solution that obtains the best tradeoff between IL and DR when compared with the state of the art. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSMCB.2009.2013723 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
lower value,disclosure risk,achieving microaggregation,best tradeoff,heuristic solution,better tradeoff,microaggregation problem,best partition,fixed-structure-stochastic-automata-based solution,microdata file,superior value,secure statistical databases,automata theory,learning artificial intelligence,databases,testing,data privacy,public policy,automatic control,indexation,statistics,np hard,computer science | Data mining,Data set,Learning automata,Computer science,Theoretical computer science,Artificial intelligence,Information privacy,Automata theory,Heuristic,Automatic control,Microdata (HTML),Partition (number theory),Machine learning | Journal |
Volume | Issue | ISSN |
39 | 5 | 1941-0492 |
Citations | PageRank | References |
7 | 0.49 | 30 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ebaa Fayyoumi | 1 | 50 | 6.77 |
| B. John Oommen | 2 | 1255 | 222.20 |