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Secure Data Processing at Scale
Citation key Maliszewski2020
Author Kajetan Maliszewski
Year 2020
Journal VLDB 2020 PhD Workshop
Note A recording of the presentationis available here: https://www.youtube.com/watch?v=wSNN64zvSFA

Presentation slides are available here: https://www.redaktion.tu-berlin.de/fileadmin/fg131/Conferences/Presentations/Maliszewski_VLDB-2020.pdf
Abstract Although the cloud is today a de-facto standard for scalable data processing, there are still many applications that cannot make use of the cloud due to data or computation privacy. Sensitive data, such as in the health do-main; and computations, such as core-business AI pipelines, grew into valuable assets that made secure data processing a hot topic in industry and academia. On one hand, the existing data processing systems prioritize performance and, to a certain level, trade users’ privacy. On the other hand, privacy-preserving data processing systems sacrifice performance. In this PhD thesis, we envision a fully secure general-purpose data processing system for the cloud. Over-all, we aim at devising: (i) algorithms that are adequate to work with very limited memory, such as the one exposed by trusted execution environments; (ii) scalable state management techniques; (iii) oblivious data-access algorithms; and (iv) privacy-preserving query optimizations techniques to speed up query execution
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