Privacy preserving extreme learning machine using additively homomorphic encryption

Recently, computational outsourcing using cloud services is getting popular for big data analysis, and many cloud sourcing providers provide machine learning platforms where we can perform various prediction and classification tasks very easily. On the other hand, there still remains a big hurdle to analyze personal big data on cloud services because the leakage of personal information is a critical issue. As a remedy for this, we propose a privacy preserving machine learning algorithm for Extreme Learning Machine (PP-ELM), which can learn from data encrypted with an additively homomorphic encryption. In the proposed outsourcing method, we consider a three-participants model consisting of data contributors, outsourced server, and data analyst. A data contributor preprocesses and encrypts data, and an outsourced server receives encrypted data and calculate hidden layer outputs using additive operations. Then, a data analyst receives the hidden outputs of ELM from the outsourced server and they are used to obtain ELM connection weights. Since the proposed outsourcing model can learn ELM over encrypted data, it is expected to mitigate a hurdle to deal with personal data on cloud services. In addition, the proposed PP-ELM allows us to learn multiple sources of personal data in a secure way, and this might lead to a better solution for a practical problem than before.