MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing
In cloud computing, the public cloud service providers (CSPs) can provide cloud storage as the primary service while providing additional machine learning (ML)-based services by using the clients' data in storage. This business model extends the border of cloud computing services and brings in new business growth possibilities. Although it is promising, the model also brings in security concerns since the public commercial cloud cannot be fully trusted. For example, the public commercial clouds may sell clients' sensitive data to the government or other companies. To address the security concerns, an immediate solution is to require clients to encrypt their datasets before outsourcing to ...
| Authors | Xu Zhou, Haoyang Chen, Xinyu Lei |
|---|---|
| Primary category | cs.CR |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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