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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Name of the periodical

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

0219-3116

Volume of the periodical

-

Issue of the periodical within the volume

-

Country of publishing house

GB - UNITED KINGDOM

Number of pages

26

Pages from-to

2301-2326

UT code for WoS article

000530839300008

EID of the result in the Scopus database

2-s2.0-85075260823