Efficient collaborative analytics with no information leakage - An idea whose time has come.

Vasiliki Kalavri

Enabling secure outsourced analytics with practical performance has been a long-standing research challenge in the database community. In this talk, I will present our work towards realizing this vision with Secrecy, a new framework for secure relational analytics in untrusted clouds. Secrecy targets offline collaborative analytics, where data owners (hospitals, companies, research institutions, or individuals) are willing to allow certain computations on their collective private data, provided that data remain siloed from untrusted entities. To ensure no information leakage and provable security guarantees, Secrecy relies on cryptographically secure Multi-Party Computation (MPC). Instead of treating MPC as a black box, like prior works, Secrecy exposes the costs of oblivious queries to the planner and employs novel logical, physical, and protocol-specific optimizations, all of which are applicable even when data owners do not participate in the computation. As a result, Secrecy outperforms state-of-the-art systems and can comfortably process much larger datasets with good performance and modest use of resources.

Vasiliki (Vasia) Kalavri is an Assistant Professor of Computer Science at Boston University, where she 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 ML, and MPC systems for private collaborative analytics. Before joining BU, Vasia was a postdoctoral fellow at ETH Zurich and received a joint PhD from KTH (Sweden) and UCLouvain (Belgium).