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We present TVA, a multi-party computation (MPC) system for secure analytics on secret-shared time series data. TVA achieves strong security guarantees in the semi-honest and malicious settings, and high expressivity by enabling complex analytics on inputs with unordered and irregular timestamps. TVA is the first system to support arbitrary composition of oblivious window operators, keyed aggregations, and multiple filter predicates, while keeping all data attributes private, including record timestamps and user-defined values in query predicates. At the core of the TVA system lie novel protocols for secure window assignment: (i) a tumbling window protocol that groups records into fixed-length time buckets and (ii) two session window protocols that identify periods of activity followed by periods of inactivity. We also contribute a new protocol for secure division with a public divisor, which may be of independent interest. We evaluate TVA on real LAN and WAN environments and show that it can efficiently compute complex window-based analytics on inputs of 2^22 records with modest use of resources. When compared to the state-of-the-art, TVA achieves up to 5.8× lower latency in queries with multiple filters and two orders of magnitude better performance in window aggregation. 

We present Secrecy, 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 data siloed even from the cloud providers. At the same time, it enables cloud providers to offer their services to clients who would have otherwise refused to perform a computation altogether or insisted that it be done on private infrastructure. Secrecy ensures no information leakage and provides provable security guarantees by employing cryptographically secure Multi-Party Computation (MPC). In Secrecy we take a novel approach to optimizing MPC execution by co-designing multiple layers of the system stack and exposing the MPC costs to the query engine. To achieve practical performance, Secrecy applies physical optimizations that amortize the inherent MPC overheads along with logical optimizations that dramatically reduce the computation, communication, and space requirements during query execution. Our multi-cloud experiments demonstrate that Secrecy improves query performance by over 1000x compared to existing approaches and computes complex analytics on millions of data records with modest use of resources.