A survey on fully homomorphic encryption with statistical applications
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2022-12
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Targino, Rodrigo dos Santos
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The amount of data generated by individuals and enterprises is growing exponentially over the last decades, which empowers the use of machine learning methods since, for statistical purposes, the more data a model can have access to, the more accurately it
will predict or represent reality. The problem emerges when the model must deal with sensitive data such as medical records, financial history, or genomic data; such cases requires additional caution in order to protect the privacy of data owners. Encrypting
sensitive data might appear a good solution at first sight, but it can considerably limit the ability to do statistical analysis. This work is a survey on Fully Homomorphic Encryption (FHE), a special kind of cryptography scheme that still permits some machine learning methods to run over encrypted data, while having strong mathematical guarantees of privacy protection.
Descrição
Trabalho de Conclusão de Curso - Rener de Souza Oliveira