Category: Publication

CASP Lab goes to SIGMOD’24 with a demo and a DaMoN paper

Members of the CASP Lab will present two recent works at the upcoming ACM SIGMOD’24 conference, in Santiago, Chile: QueryShield: Cryptographically Secure Analytics in the Cloud, was accepted at the SIGMOD’24 Demos track. In situ neighborhood sampling for large-scale GNN training, was accepted at the Data Management on New Hardware (DaMoN’24) workshop.

Paper accepted at EDBT’24

Our paper “Crayfish: Navigating the Labyrinth of Machine Learning Inference in Stream Processing Systems” was accepted for presentation at the 27th International Conference on Extending Database Technology (EDBT ’24).  We contribute a principled benchmarking framework to help navigate the chaos in streaming ML inference.

Paper accepted at USENIX Security ’23

Our paper “TVA: A multi-party computation system for secure and expressive time series analytics”, authored by Muhammad Faisal, Jerry Zhang, John Liagouris, Vasiliki Kalavri, and Mayank Varia, was accepted for publication at USENIX Security ’23. TVA is a multi-party computation system for secure analytics on secret-shared time series data that achieves high expressivity, by enabling […]

“Secrecy: Secure collaborative analytics in untrusted clouds” accepted at NSDI’23

Our paper, “Secrecy: Secure collaborative analytics in untrusted clouds”, by John Liagouris, Vasiliki Kalavri, Muhammad Faisal, and Mayank Varia, has been accepted for presentation at NSDI’23! Secrecy is a system for privacy-preserving collaborative analytics as a service. Secrecy allows multiple data holders to contribute their data towards a joint analysis in the cloud, while keeping […]

Papers accepted at DEEM and aiDM SIGMOD’22 workshops

The following papers were accepted for presentation at upcoming SIGMOD’22 workshops: Evaluating Model Serving Strategies over Streaming Data by Sonia Horchidan, Emmanouil Kritharakis, Vasiliki Kalavri and Paris Carbone was accepted at the 6th Workshop on Data Management for End-to-End Machine Learning (DEEM’22). GCNSplit: Bounding the State of Streaming Graph Partitioning by Michał Zwolak, Zainab Abbas, Sonia Horchidan, […]

Secrecy: New preprint available

In out latest work, we study the problem of composing and optimizing relational query plans under secure multi-party computation (MPC). MPC enables mutually distrusting parties to jointly compute arbitrary functions over private data, while preserving data privacy from each other and from external entities. We have released a new preprint on the arxiv, where we […]