Privacy-Preserving OLAP via Modeling and Analysis of Query Workloads: Innovative Theories and Theorems

35TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT, SSDBM 2023（2023）

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摘要

This paper proposes innovative theories and theorems in the context of a state-of-the-art paper that computes privacy-preserving OLAP cubes via modeling and analyzing query workloads. The work contributes to actual literature by devising a solid theoretical framework that can be used for future optimization opportunities.

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关键词

Privacy-Preserving OLAP,QueryWorkloads for Privacy-Preserving OLAP Data,Theoretical Analysis

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