Cloud service providers offer robust infrastructure for rent to organizations of all kinds. High stakes applications, such as the ones in defense and healthcare, are turning to the public cloud for a cost-effective, geographically distributed, always available solution to their hosting needs. Many such users are unwilling or unable to delegate their data to this third-party infrastructure. In this demonstration, we introduce RESCU-SQL, a zero-trust platform for resilient and secure SQL querying outsourced to one or more cloud service providers. RESCU-SQL users can query their DBMS using cloud infrastructure alone without revealing their private records to anyone. It does so by executing the query over secure multiparty computation. We call this system zero trust because it can tolerate any number of malicious servers provided one of them remains honest. Our demo will offer an interactive dashboard with which attendees can observe the performance of RESCU-SQL deployed on several in-cloud nodes for the TPC-H benchmark. Attendees can select a computing party and inject messages from it to explore how quickly it detects and reacts to a malicious party. This is the first SQL system to support all-but-one maliciously secure querying over a semi-honest coordinator for efficiency.

Individuals and organizations are using databases to store personal information at an unprecedented rate. This creates a quandary for data providers. They are responsible for protecting the privacy of individuals described in their database. On the other hand, data providers are sometimes required to provide statistics about their data instead of sharing it wholesale with strong assurances that these answers are correct and complete such as in regulatory filings for the US SEC and other goverment organizations. We introduce a system, ZKSQL , that provides authenticated answers to ad-hoc SQL queries with zero-knowledge proofs. Its proofs show that the answers are correct and sound with respect to the database's contents and they do not divulge any information about its input records. This system constructs proofs over the steps in a query's evaluation and it accelerates this process with authenticated set operations. We validate the efficiency of this approach over a suite of TPC-H queries and our results show that ZKSQL achieves two orders of magnitude speedup over the baseline.

Existing work on privacy-preserving machine learning with Secure Multiparty Computation (MPC) is almost exclusively focused on model training and on inference with trained models, thereby overlooking the important data pre-processing stage. In this work, we propose the first MPC based protocol for private feature selection based on the filter method, which is independent of model training, and can be used in combination with any MPC protocol to rank features. We propose an efficient feature scoring protocol based on Gini impurity to this end. To demonstrate the feasibility of our approach for practical data science, we perform experiments with the proposed MPC protocols for feature selection in a commonly used machine-learning-as-a-service configuration where computations are outsourced to multiple servers, with semi-honest and with malicious adversaries. Regarding effectiveness, we show that secure feature selection with the proposed protocols improves the accuracy of classifiers on a variety of real-world data sets, without leaking information about the feature values or even which features were selected. Regarding efficiency, we document runtimes ranging from several seconds to an hour for our protocols to finish, depending on the size of the data set and the security settings.