SPPOLAP: Computing Privacy-Preserving OLAP Data Cubes Effectively and Efficiently Algorithms, Complexity Analysis and Experimental Evaluation

摘要

This paper provides significant contributions in the line of the so-calledprivacy-preservingOLAP research area, via extending the previous SPPOLAP’s results provided recently. SPPOLAP is a state-of-the-art algorithm whose main goal consists incomputing privacy-preservingOLAPdata cubes effectively and efficiently. The main innovations carried-out by SPPOLAP are represented by the novelprivacyOLAPnotionand the flexible adoption of sampling-based techniques in order to achieve the final privacy-preserving data cube. In line with the main SPPOLAP’s results, this paper significantly extends the previous research efforts by means of the following contributions:(i)complete algorithms of the whole SPPOLAP algorithmic framework;(ii)complexity analysis and results;(iii)comprehensive experimental analysis of SPPOLAP against real-life multidimensional data cubes, according to several experimental parameters. These contributions nice-fully complete the state-of-the-art SPPOLAP’s results.