Seow, EthanBaum, EliBuxbaum, SamFaisal, Muhammad SajidLiagouris, JohnKalavri, VasilikiVaria, Mayank2024-10-242024-10-242024-06-09Ethan Seow, Yan Tong, Eli Baum, Sam Buxbaum, Muhammad Faisal, John Liagouris, Vasiliki Kalavri, and Mayank Varia. 2024. QueryShield: Cryptographically Secure Analytics in the Cloud. In Companion of the 2024 International Conference on Management of Data (SIGMOD/PODS '24). Association for Computing Machinery, New York, NY, USA, 436–439. https://doi.org/10.1145/3626246.3654749ISBN 979-8-4007-0422-2https://hdl.handle.net/2144/49420We present a demonstration of QueryShield, a service for streamlined, cryptographically secure data analytics in the cloud. With QueryShield, data analysts can advertise analysis descriptions to data owners, who may agree to participate in a computation for profit or for the greater good, provided that their data remain private. QueryShield supports relational and time series analytics with provable data privacy guarantees using secure multi-party computation (MPC). At the same time, it makes MPC accessible to non-expert users by offering a familiar web interface and fully-automated orchestration of cryptographic computations. We devise three demonstration scenarios for conference attendees: (i) an interactive survey of private employment information to estimate the industry-academia wage gap in the data management community, (ii) a relational analysis that identifies credit score anomalies in sensitive customer data from multiple credit agencies, and (iii) a medical use case that assesses the effectiveness of insulin dose frequency in a patient cohort.en-US© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. This article has been published under a Read & Publish Transformative Open Access (OA) Agreement with ACM.https://creativecommons.org/licenses/by/4.0/Information SystemsSecurity and PrivacyCryptographyData Management SystemsArticle10.1145/3626246.3654749