Seminar: Cryptographically secure analytics in the Cloud
Date and time: 24 September 2024, 10:00-11:00 CEST
Speaker: Vasiliki Kalavri, Boston University
Title: Cryptographically secure analytics in the Cloud
Where: Digital Futures hub, Osquars Backe 5, floor 2 at KTH main campus OR Zoom
Directions: https://www.digitalfutures.kth.se/contact/how-to-get-here/
OR
Zoom: https://kth-se.zoom.us/j/69560887455
Host: Paris Carbone, parisc@kth.se
Abstract: Enabling secure outsourced analytics with practical performance has been a long-standing research challenge in the data management and systems communities. In this talk, I will present recent work that allows multiple data holders to contribute their data towards a joint analysis in the cloud, while keeping the data siloed even from the cloud providers. I will give an overview of the QueryShield service that offers secure relational and time series analytics as-a-service, using multi-party computation (MPC). QueryShield relies on two novel MPC systems developed in our lab: (i) the SECRECY system for relational analytics, that exposes the costs of oblivious queries to the planner and employs novel logical, physical, and protocol-specific optimizations, and (ii) the TVA system for time series analytics, that introduces protocols for secure window aggregation on private inputs with unordered and irregular timestamps. Finally, I will describe challenges and ongoing research efforts towards developing an expressive and scalable MPC analytics framework with configurable security guarantees.
Bio: Vasiliki (Vasia) Kalavri is an Assistant Professor of Computer Science at Boston University, where she co-leads the Complex Analytics and Scalable Processing (CASP) Systems lab. Vasia and her team enjoy doing research on multiple aspects of (distributed) data-centric systems. Recently, they have been working on self-managed systems for data stream processing, systems for scalable graph Machine Learning, and systems for secure collaborative analytics. Before joining BU, Vasia was a postdoctoral fellow at ETH Zurich and received a joint PhD from KTH Royal Institute of Technology and UCLouvain (Belgium). Vasia’s work has been recognized with several awards, including an IBM Innovation Award for her PhD Dissertation in 2017 and the SIGMOD Systems Award in 2023. Vasia’s research lab has received funding from the NSF and industry awards from Google, Samsung, and RedHat.