Perspectives on the Feasibility and Adoption of Fully Homomorphic Encryption - A Fully Homomorphic Encryption Application on Spam Detection with Machine Learning Models

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OrientadorRibeiro, Carlos Nuno da Cruz
Orcid do Orientador0000-0002-6080-0996
AutorPinto, Adriano Roberto
Orcid do Autor0009-0000-7654-1442
Membro da bancaCaleiro, Carlos Manuel Costa Lourenço
Membro da bancaSilva, Fernando Henrique Côrte-Real Mira da
Orcid Membro da banca0000-0001-5587-6585
Orcid Membro da banca0000-0001-9959-3798
Data de Acesso2026-01-23T15:31:24Z
Ano de publicação2024-12-03
AbstractA adoção da computação em nuvem resultou em incidentes de segurança, como vazamentos de dados nos setores público e privado. Provedores operam sem transparência, descumprem re-gulamentações e exploram dados privados para publicidade e treinamento de modelos de inteligência artificial. As empresas de tecnologia também colaboram com agências de inteligência para vigiar ilegalmente indivíduos e governos, compartilhando dados privados, como as mensagens de email. Este estudo analisa a viabilidade da Criptografia Completamente Homomórfica (CCH) como solução para essas questões de segurança, com foco na detecção de spam e provedores de email como objetos representativos no universo da computação feita por terceiros em dados privados. A CCH mantém a privacidade ao habilitar computações enquanto os dados estão criptografados, mas exige elevados recursos computacionais. O desenvolvimento de aplicativos baseados em CCH é complexo e requer aplicação de conceitos matemáticos e criptográficos avançados. Este trabalho avalia a viabilidade da CCH por meio de experimentos em detecção de spam implementando o Fully Homomorphic Encryption Spam Detector (FHE-SD), um aplicativo que utiliza a biblioteca Concrete-ML para abstrair a complexidade da CCH e simplificar sua adoção. O ambiente experimental comporta um dispositivo com hardware limitado, escolhido para verificar se a CCH pode funcionar sem hardware especializado. Para melhores resultados, o FHE-SD tem suporte à detecção de spam usando algoritmos de aprendizado de máquina, que são medidas anti-spam comuns. Quatro modelos de aprendizado de máquina foram implementados no FHE-SD, em suas versões CCH e em dados puros, permitindo métricas e comparações de desempenho em relação às abordagens tradicionais.
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ResumoThe general adoption of cloud computing resulted in frequent security incidents, including data breaches in both the public and private sectors. Cloud service providers operate without transparency, fail to comply with regulations, and exploit private data for targeted advertising and training artificial intelligence models. The major tech companies have also been found to collaborate with intelligence agencies to illegally surveil individuals and governments by sharing private data, such as email communications. This study analyses the feasibility of Fully Homomorphic Encryption (FHE) as a solution to these security and privacy concerns, focusing on spam detection and email providers as representative subjects in the universe of outsourced computations on users’ private data. FHE maintains data privacy by enabling computations while the data is still encrypted, but it demands substantial computing and memory resources. The development of FHE-based applications is complex, requiring advanced knowledge of mathematical and cryptographic concepts. This work assesses the feasibility of FHE through experiments in spam detection by implementing Fully Homomorphic Encryption Spam Detector (FHE-SD), an application using the Concrete-ML libraries, which abstracts the complexity of FHE and simplifies its adoption. The experimental environment is a device with limited hardware resources, chosen to test if FHE can function without specialized hardware. For meaningful results, FHE-SD supports spam detection using machine learning algorithms, which are commonly used for spam detection. Four machine learning models are implemented in FHE-SD, in their FHE and on-clear versions, enabling various metrics and performance comparisons against traditional approaches.
CitaçãoPinto, A. R. (2024). Perspectives on the Feasibility and Adoption of Fully Homomorphic Encryption - A Fully Homomorphic Encryption Application on Spam Detection with Machine Learning Models. Instituto Superior Técnico.
Identificador ISNI0000 0004 1794 1114
URL da página da instituiçãohttps://tecnico.ulisboa.pt/
Identificador ROR03db2by73
URIhttps://deposita.ibict.br/handle/deposita/836
Publicação originalhttps://scholar.tecnico.ulisboa.pt/records/iAaA4G3H7Wz7loLNKUfnTKY33xylUdnQbv3O
Identificador WikidataQ1636837
Área de conhecimento CNPqCiências exatas e da terra
IdiomaInglês
InstituiçãoInstituto Superior Técnico da Universidade de Lisboa
PaísPortugal
Grau do cursoMestrado
Departamento do cursoMatemática
Nome do cursoMestrado Bolonha em Segurança de Informação e Direito no Ciberespaço
Tipo do cursoAcadêmico
Natureza jurídica da instituiçãoInstituição pública
Nome do programa de pós-graduaçãoMestrado Bolonha em Segurança de Informação e Direito no Ciberespaço
Tipo da instituiçãoUniversidade
Tipo de acessoAcesso aberto
Palavra ChaveFully Homomorphic Encryption
Palavra ChaveMachine Learning
Palavra ChaveSpam Detection
Palavra ChavePerformance
Palavra ChavePrivacy
Palavra ChaveSecurity
Palavra chave em outro idiomaCriptografia Completamente Homomórfica
Palavra chave em outro idiomaAprendizado de Máquina
Palavra chave em outro idiomaDetecção de Spam
Palavra chave em outro idiomaDesempenho
Palavra chave em outro idiomaPrivacidade
Palavra chave em outro idiomaSegurança
TítuloPerspectives on the Feasibility and Adoption of Fully Homomorphic Encryption - A Fully Homomorphic Encryption Application on Spam Detection with Machine Learning Models
Titúlo AlternativoPerspectivas sobre a Viabilidade e Adoção da Criptografia Totalmente Homomórfica - Uma aplicação da Criptografia Totalmente Homomórfica na Detecção de Spam com Modelos de Aprendizado de Máquina
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