MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing
arXiv:2607.08197v1 Announce Type: cross Abstract: 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
The proliferation of cloud-based AI services and growing concerns over data privacy mean that secure ML queries are an increasingly critical current need.
This development addresses a fundamental tension between leveraging cloud AI capabilities and maintaining data confidentiality, which is vital for businesses and governments hesitant to use public clouds.
It introduces a framework that allows for machine learning computations on encrypted data in untrusted cloud environments, reducing the risk of data exploitation by cloud providers.
- · Cloud service users with sensitive data
- · Cybersecurity companies
- · Industries with strict data compliance (e.g., healthcare, finance)
- · Cloud providers with weak data handling policies
- · Actors relying on illicit data access
Increased adoption of cloud-based AI services by data-sensitive organizations due to enhanced security.
Reduced bargaining power of major public cloud providers as data security becomes less of a differentiator and more of a standard expectation.
Potential for sovereign AI initiatives to leverage similar privacy-preserving technologies to secure their domestic data and AI infrastructure without full reliance on a single stack.
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Read at arXiv cs.LG