Protecting Sensitive Tabular Data in Hybrid Clouds
Maya Anderson, Gidon Gershinsky, Eliot Salant, Salvador Garcia

TL;DR
This paper demonstrates how to securely perform large-scale data analytics on sensitive data stored in hybrid cloud environments using encryption techniques that ensure privacy, integrity, and efficiency without vendor lock-in.
Contribution
It introduces a method for running analytics on encrypted data in hybrid clouds using Apache Parquet Modular Encryption, with an innovation to reduce performance overhead from key management calls.
Findings
Secure analytics on encrypted data is feasible with minimal performance loss.
PME provides privacy, integrity, and granular access control in hybrid cloud settings.
The approach prevents vendor lock-in and maintains data security.
Abstract
Regulated industries, such as Healthcare and Finance, are starting to move parts of their data and workloads to the public cloud. However, they are still reluctant to trust the public cloud with their most sensitive records, and hence leave them in their premises, leveraging the hybrid cloud architecture. We address the security and performance challenges of big data analytics using a hybrid cloud in a real-life use case from a hospital. In this use case, the hospital collects sensitive patient data and wants to run analytics on it in order to lower antibiotics resistance, a significant challenge in healthcare. We show that it is possible to run large-scale analytics on data that is securely stored in the public cloud encrypted using Apache Parquet Modular Encryption (PME), without significant performance losses even if the secret encryption keys are stored on-premises. PME is a…
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Taxonomy
TopicsCryptography and Data Security · Privacy-Preserving Technologies in Data · Cloud Data Security Solutions
