Protecting confidential data in cloud environments - Guimarães, Ronny Peterson (2024)
The growing adoption of cloud computing has brought significant challenges for protecting sensitive data, particularly when such data is stored and processed in shared public infrastructures. This work addresses these challenges by proposing solutions for the protection of sensitive data in cloud environments, focusing on methods that ensure privacy without compromising efficiency in data access and manipulation. This thesis presents the Vallum platform, which leverages hardware-based security (Intel SGX) to protect sensitive data, alongside an optimized version that adopts selective protection through vertical partitioning, aiming to improve performance. The research examines the impact of different privacy-preserving mechanisms on system performance, particularly in terms of throughput and response time, and evaluates the trade-offs between implementing robust security measures and the need to maintain processing efficiency. Through detailed experimental testing, the results show that while full protection with SCONE/SGX (Vallum 1) leads to significant performance degradation, the selective protection approach (Vallum 2) provides a more effective balance, improving system scalability without compromising security. These results provide a foundation for understanding how cloud database systems can balance confidentiality requirements with performance demands, making them more suitable for large-scale applications. Furthermore, this work contributes to the field with academic publications, including presentations at renowned international conferences and journal articles. The first version of the Vallum platform was developed within the context of the international ATMOSHP ERE project, a collaboration between research institutions and companies in Brazil and Europe, validating its applicability in real-world scenarios. Therefore, this research proposes solutions that ensure the privacy protection of sensitive data in cloud environments while maintaining efficiency in processing large volumes of data, enabling more secure use of cloud computing by organizations.