Privacy-Preserving Machine Learning on Apache Spark

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Brito, CV (Author) Other; Ferreira, PG (Author) FCUP; Portela, BL (Author) FCUP; Oliveira, RC (Author) Other; Paulo, JT (Author) Other

Journal

Title: IEEE Access

Vol. 11

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-B3G

DOI: 10.1109/access.2023.3332222

Abstract (EN): 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 the performance of privacy-preserving solutions without exposing the protocol to pervasive ML attacks. In more detail, we propose Soteria, a system for distributed privacy-preserving ML that leverages Trusted Execution Environments (e.g. Intel SGX) to run computations over sensitive information in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41% when compared to previous related work. Our protocol is accompanied by a security proof and a discussion regarding resilience against a wide spectrum of ML attacks.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 24