| Title | Secure Matrix Operations for Machine Learning Classifications Over Encrypted Data in Post Quantum Industrial IoT |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Kjamilji, Artrim, Levi, Albert, Savas, Erkay, Güney, Osman Berke |
| Conference Name | 2021 International Symposium on Networks, Computers and Communications (ISNCC) |
| Date Published | oct |
| Keywords | classification, Classification algorithms, composability, compositionality, cryptography, cybersecurity, Deep Learning, Health, Industrial Informatics, machine learning, machine learning algorithms, Post-quantum cryptography, privacy, privacy preserving algorithms, pubcrawl, quantum computing, secure IoT, secure matrix operations, support vector machine classification, theoretical cryptography |
| Abstract | We tackle the problem where a server owns a trained Machine Learning (ML) model and a client/user has an unclassified query that he wishes to classify in secure and private fashion using the server's model. During the process the server learns nothing, while the user learns only his final classification and nothing else. Since several ML classification algorithms, such as deep neural networks, support vector machines-SVM (and hyperplane decisions in general), Logistic Regression, Naive Bayes, etc., can be expressed in terms of matrix operations, initially we propose novel secure matrix operations as our building blocks. On top of them we build our secure and private ML classification algorithms under strict security and privacy requirements. As our underlying cryptographic primitives are shown to be resilient to quantum computer attacks, our algorithms are also suitable for the post-quantum world. Our theoretical analysis and extensive experimental evaluations show that our secure matrix operations, hence our secure ML algorithms build on top of them as well, outperform the state of the art schemes in terms of computation and communication costs. This makes our algorithms suitable for devices with limited resources that are often found in Industrial IoT (Internet of Things) |
| DOI | 10.1109/ISNCC52172.2021.9615794 |
| Citation Key | kjamilji_secure_2021 |
Secure Matrix Operations for Machine Learning Classifications Over Encrypted Data in Post Quantum Industrial IoT