Outsourcing analyses on privacy-protected multivariate categorical data stored in untrusted clouds

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU134777" target="_blank" >RIV/00216305:26220/19:PU134777 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10115-019-01424-4" target="_blank" >https://link.springer.com/article/10.1007/s10115-019-01424-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10115-019-01424-4" target="_blank" >10.1007/s10115-019-01424-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Outsourcing analyses on privacy-protected multivariate categorical data stored in untrusted clouds

Popis výsledku v původním jazyce

Outsourcing data storage and computation to the cloud is appealing due to the cost savings it entails. However, when the data to be outsourced contain private information, appropriate protection mechanisms should be implemented by the data controller. Data splitting, which consists of fragmenting the data and storing them in separate clouds for the sake of privacy preservation, is an interesting alternative to encryption in terms of flexibility and efficiency. However, multivariate analyses on data split among various clouds are challenging, and they are even harder when data are nominal categorical (i.e., textual, non-ordinal), because the standard arithmetic operators cannot be used. In this article, we tackle the problem of outsourcing multivariate analyses on nominal data split over several honest-but-curious clouds. Specifically, we propose several secure protocols to outsource to multiple clouds the computation of a variety of multivariate analyses on nominal categorical data (frequency-based and semantic-based). Our protocols have been designed to outsource as much workload as possible to the clouds, in order to retain the cost-saving benefits of cloud computing while ensuring that the outsourced stay split and hence privacy-protected versus the clouds. The experiments we report on the Amazon cloud service show that by using our protocols the controller can save nearly all the runtime because it can integrate partial results received from the clouds with very little computation.

Název v anglickém jazyce

Outsourcing analyses on privacy-protected multivariate categorical data stored in untrusted clouds

Popis výsledku anglicky

Outsourcing data storage and computation to the cloud is appealing due to the cost savings it entails. However, when the data to be outsourced contain private information, appropriate protection mechanisms should be implemented by the data controller. Data splitting, which consists of fragmenting the data and storing them in separate clouds for the sake of privacy preservation, is an interesting alternative to encryption in terms of flexibility and efficiency. However, multivariate analyses on data split among various clouds are challenging, and they are even harder when data are nominal categorical (i.e., textual, non-ordinal), because the standard arithmetic operators cannot be used. In this article, we tackle the problem of outsourcing multivariate analyses on nominal data split over several honest-but-curious clouds. Specifically, we propose several secure protocols to outsource to multiple clouds the computation of a variety of multivariate analyses on nominal categorical data (frequency-based and semantic-based). Our protocols have been designed to outsource as much workload as possible to the clouds, in order to retain the cost-saving benefits of cloud computing while ensuring that the outsourced stay split and hence privacy-protected versus the clouds. The experiments we report on the Amazon cloud service show that by using our protocols the controller can save nearly all the runtime because it can integrate partial results received from the clouds with very little computation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

0219-3116

Svazek periodika

-

Číslo periodika v rámci svazku

-

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

26

Strana od-do

2301-2326

Kód UT WoS článku

000530839300008

EID výsledku v databázi Scopus

2-s2.0-85075260823