Autor(es)

de Souza, Lucas Airam C. ; Rebello, Gabriel Antonio F. ; Camilo, Gustavo F. ; Guimaraes, Lucas C. B. ; Duarte, Otto Carlos M. B. ; IEEE Comp Soc

• Tipo de documento: Artigo Científico
• Fonte: 2020 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2020); v. N/A, p. 8-pg., 2020-01-01.

Resumo

The effectiveness of machine learning systems depends heavily on the relevance of the training data. Usually, the collected data is sensitive and private because it comes from devices and sensors used in people's daily lives. The General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in California, and China's Cybersecurity Law put the current approach at risk, as it prohibits centralized remote processing of sensitive data collected in a distributed manner. This paper proposes a distributed machine learning system based on local random forest algorithms created with shared decision trees through the blockchain. The results show that the proposed approach equals or exceeds the results obtained with the use of random forests with only local data. Furthermore, the proposal increases the detection of new attacks when the domains have different threat distributions. (AU)

• Processo FAPESP: 18/23292-0 - Projeto ACCRUE-SFI: infraestrutura avançada e colaborativa de pesquisa para internet do futuro segura
• Beneficiário: Otto Carlos Muniz Bandeira Duarte
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 14/50937-1 - INCT 2014: da Internet do Futuro
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático