Privacy-Preserving Machine Learning on Apache Spark
Cláudia V. Brito,
Pedro G. Ferreira,
Bernardo L. Portela
et al.
Abstract:The adoption of third-party machine learning (ML) cloud services is highly dependent on the security guarantees and the performance penalty they incur on workloads for model training and inference. This paper explores security/performance trade-offs for the distributed Apache Spark framework and its ML library. Concretely, we build upon a key insight: in specific deployment settings, one can reveal carefully chosen non-sensitive operations (e.g. statistical calculations). This allows us to considerably improve… Show more
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