This paper provides significant contributions in the line of the so-called privacy-preserving OLAP research area, via extending the previous SPPOLAP's results provided recently. SPPOLAP is a state-of-the-art algorithm whose main goal consists in computing privacy-preserving OLAP data cubes effectively and efficiently. The main innovations carried-out by SPPOLAP are represented by the novel privacy OLAP notion and 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.

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

Cuzzocrea, Alfredo
2020-01-01

Abstract

This paper provides significant contributions in the line of the so-called privacy-preserving OLAP research area, via extending the previous SPPOLAP's results provided recently. SPPOLAP is a state-of-the-art algorithm whose main goal consists in computing privacy-preserving OLAP data cubes effectively and efficiently. The main innovations carried-out by SPPOLAP are represented by the novel privacy OLAP notion and 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.
Big Data
OLAP
Privacy of Big Data
Privacy-Preserving OLAP
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/340259
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