Mayank Varia, Research Scientist, Boston University
This talk describes our experience deploying a web application that securely analyzed the (sensitive!) salary data of 112,600 employees in the Greater Boston Area in order to calculate pay disparity across gender and race. We use a cryptographic tool called secure multi-party computation in order to balance transparency with confidentiality; this tool allows us to analyze the data without ever learning any person or company's salary information. Our experiences demonstrate that cryptographically secure data analysis over sensitive data can provide significant social benefits in contexts where data sharing is constrained or prevented by legal, ethical, or privacy restrictions.
Dr. Mayank Varia is a Research Scientist at Boston University. His research interests span theoretical and applied cryptography and their application to problems throughout computer science. He currently directs the Modular Approach to Cloud Security project (bu.edu/macs), an NSF Frontier project to build cloud computing systems with meaningful, multi-layered, composable security guarantees. He received a PhD in Mathematics from MIT for his work on cryptographically secure program obfuscation.
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