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Activity Number: 633 - Foundations of Data Science: Privacy-Preserving Inference
Type: Invited
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #300552 Presentation
Title: Privacy-Preserving Technologies Meet Machine Learning
Author(s): Jeannette Wing*
Companies: Columbia University, Data Science Institute
Keywords: Privacy; Differential Privacy; Secure Multi-Party Computation; Homomorphic Encryption

This talk will relate technologies that can help preserve the privacy of data to methods in machine learning. These technologies include statistics-based approaches, such as differential privacy; cryptographic approaches, such as secure multi-party computation and homomorphic encryption; and hardware approaches, such as secure enclaves.

Authors who are presenting talks have a * after their name.

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