A new scheme called MLQENABLER has been proposed to enable secure machine learning queries on encrypted databases within cloud computing environments. This approach addresses the security concerns arising from the use of public cloud service providers by allowing clients to encrypt their data before outsourcing it. MLQENABLER utilizes an index-aid method to maintain both security and machine learning capabilities, with initial experiments indicating acceptable security levels and only minor performance degradation. AI
IMPACT Enhances security for machine learning applications utilizing cloud-based encrypted data.
RANK_REASON The cluster describes a research paper detailing a new scheme for secure machine learning queries on encrypted databases.
- arXiv
- cloud computing
- Encrypted Databases
- Google Cloud Storage
- government
- machine learning
- MLQENABLER
- Public Cloud Service Providers
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