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 […]
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 […]
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, […]
The paper “The Non-Expert Tax: Quantifying the cost of auto-scaling in Cloud-based data stream analytics.” by Yuanli Wang, Baiqing Lyu, and Vasiliki Kalavri, has been accepted for presentation at the BiDEDE’22 workshop.
Our paper “A New Benchmark Harness for Systematic and Robust Evaluation of Streaming State Stores” has been accepted for presentation at the EuroSys’22 conference.
Our paper “Learning on streaming graphs with experience replay” has been accepted to appear at the 2022 ACM/SIGAPP Symposium on Applied Computing (SAC’22). This is a collaboration with Massimo Perini (University of Edinburgh), Giorgia Ramponi (ETH Zurich), and Paris Carbone (KTH Royal Institute of Technology). See the preprint pdf here.
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 […]