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The paper introduces MLQENABLER, a novel scheme designed to facilitate secure machine learning queries over encrypted databases in cloud computing environments. This approach addresses significant security concerns associated with public cloud services by allowing clients to encrypt their datasets while still enabling effective machine learning operations. Initial experiments indicate that MLQENABLER maintains an acceptable level of security with only minimal degradation in machine learning performance, highlighting its practical viability for secure cloud-based ML applications.
MLQENABLER allows secure machine learning queries on encrypted databases, striking a balance between data privacy and ML performance.
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 the cloud. However, if a database is formally encrypted, then the database contains only pseudorandom numbers, making it impossible to enable ML over it. In this project, we propose MLQENABLER (ML Queries Enabler) scheme to enable secure ML queries over encrypted database in cloud storage. MLQENABLER employs an index-aid approach to achieve security and ML capability simultaneously. Our initial experiments show that MLQENABLER achieves an acceptable security level while incurring only a slight ML performance degradation.
