MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing
ArXiv cs.LG ·
01 / At a Glance
MLQENABLER is a research framework enabling machine learning queries to run directly over encrypted databases in cloud environments, addressing the challenge of performing analytics on sensitive data without decryption. The approach combines secure multi-party computation and homomorphic encryption techniques to maintain data confidentiality while supporting ML operations, relevant for regulated industries requiring strong data protection during analytics.
02 / Full Analysis
MLQENABLER is a research framework enabling machine learning queries to run directly over encrypted databases in cloud environments, addressing the challenge of performing analytics on sensitive data without decryption. The approach combines secure multi-party computation and homomorphic encryption techniques to maintain data confidentiality while supporting ML operations, relevant for regulated industries requiring strong data protection during analytics.
03 / QM Perspective
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Original source
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