QueryShield: Cryptographically Secure Analytics in the Cloud
Authors: 
Ethan Seow, Yan Tong, Eli Baum, Sam Buxbaum, Muhammad Faisal, John Liagouris, Vasiliki Kalavri, Mayank VariaAuthors Info & Claims
Ethan Seow, Yan Tong, Eli Baum, Sam Buxbaum, Muhammad Faisal, John Liagouris, Vasiliki Kalavri, Mayank VariaAuthors Info & ClaimsPages 436 - 439
Abstract
We 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.
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Index Terms
- QueryShield: Cryptographically Secure Analytics in the Cloud
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Information & Contributors
Information
Published In
June 2024
694 pages
ISBN:9798400704222
DOI:10.1145/3626246
- General Chairs:
- Pablo Barcelo,
- Nayat Sanchez-Pi,
- Program Chairs:
- Alexandra Meliou,
- S. Sudarshan
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 June 2024
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- National Sience Foundation
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SIGMOD/PODS '24
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