STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING

STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING

In this paper we present the State of the Art in Secure Multiparty Computation Protocols to Privacy Preserving Data Mining. Basic definitions on the main topics are presented as some proposed protocols in the reviewed literature. We also briefly discuss the authors contribution under practical integ...

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Main Authors: Walter Priesnitz Filho, Carlos Nuno da Cruz Ribeiro
Format: Article
Language:Portuguese
Published: Associação Acadêmica de Propriedade Intelectual 2017-12-01
Series:Revista GEINTEC
Online Access:http://www.revistageintec.net/index.php/revista/article/view/1213
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spelling doaj-f6fc858ecf7a420db5747cbb6fc9cfd32020-11-24T22:35:58ZporAssociação Acadêmica de Propriedade IntelectualRevista GEINTEC2237-07222017-12-01744131414810.7198/geintec.v7i4.1213664STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MININGWalter Priesnitz Filho0Carlos Nuno da Cruz Ribeiro1Universidade Federal de Santa Maria Instituto Superior Técnico, Universidade de LisboaInstituto Superior Técnico, Universidade de LisboaIn this paper we present the State of the Art in Secure Multiparty Computation Protocols to Privacy Preserving Data Mining. Basic definitions on the main topics are presented as some proposed protocols in the reviewed literature. We also briefly discuss the authors contribution under practical integration perspective and points some issues on using SMC in PPDM.http://www.revistageintec.net/index.php/revista/article/view/1213
collection DOAJ
language Portuguese
format Article
sources DOAJ
author Walter Priesnitz Filho
Carlos Nuno da Cruz Ribeiro
spellingShingle Walter Priesnitz Filho
Carlos Nuno da Cruz Ribeiro
STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING
Revista GEINTEC
author_facet Walter Priesnitz Filho
Carlos Nuno da Cruz Ribeiro
author_sort Walter Priesnitz Filho
title STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING
title_short STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING
title_full STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING
title_fullStr STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING
title_full_unstemmed STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING
title_sort state of the art of secure multiparty computation for privacy preserving data mining
publisher Associação Acadêmica de Propriedade Intelectual
series Revista GEINTEC
issn 2237-0722
publishDate 2017-12-01
description In this paper we present the State of the Art in Secure Multiparty Computation Protocols to Privacy Preserving Data Mining. Basic definitions on the main topics are presented as some proposed protocols in the reviewed literature. We also briefly discuss the authors contribution under practical integration perspective and points some issues on using SMC in PPDM.
url http://www.revistageintec.net/index.php/revista/article/view/1213
work_keys_str_mv AT walterpriesnitzfilho stateoftheartofsecuremultipartycomputationforprivacypreservingdatamining
AT carlosnunodacruzribeiro stateoftheartofsecuremultipartycomputationforprivacypreservingdatamining
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