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Experimenting and Assessing a Distributed Privacy-Preserving OLAP over Big DataFramework: Principles, Practice, and Experiences

Abstract : OLAP is an authoritative analytical tool in the emerging big data analytics context, with particular regards to the target distributed environments (e‧g., Clouds). Here, privacypreserving OLAP-based big data analytics is a critical topic, with several amenities in the context of innovative big data application scenarios like smart cities, social networks, bio-informatics, and so forth. The goal is that of providing privacy preservation during OLAP analysis tasks, with particular emphasis on the privacy of OLAP aggregates. Following this line of research, in this paper we provide a deep contribution on experimenting and assessing a state-of-the-art distributed privacy-preserving OLAP framework, named as SPPOLAP, whose main benefit is that of introducing a completely-novel privacy notion for OLAP data cubes.
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https://hal.univ-lorraine.fr/hal-03284134
Contributor : Maira Nassau <>
Submitted on : Monday, July 19, 2021 - 3:29:30 PM
Last modification on : Saturday, September 11, 2021 - 3:37:01 AM

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Alfredo Cuzzocrea, Vincenzo de Maio, Edoardo Fadda. Experimenting and Assessing a Distributed Privacy-Preserving OLAP over Big DataFramework: Principles, Practice, and Experiences. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Jul 2020, Madrid, France. ⟨10.1109/compsac48688.2020.00-69⟩. ⟨hal-03284134⟩

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