[Theory Lunch 04/09] Rachel Cummings: Attribute Privacy: Framework and Mechanisms

[Haoming Li]

USC CS Theory Lunch:

Attribute Privacy: Framework and Mechanisms
Speaker: Rachel Cummings (Columbia University)
Time: 04/09/21 11:45am PST
Location: https://usc.zoom.us/j/94386654763

Abstract:

(joint work with Wanrong Zhang and Olya Ohrimenko)
Ensuring the privacy of training data is a growing concern since many machine learning models are trained on confidential and potentially sensitive data. Much attention has been devoted to methods for protecting individual privacy during analyses of large datasets. However in many settings, global properties of the dataset may also be sensitive (e.g., mortality rate in a hospital rather than presence of a particular patient in the dataset). In this work, we depart from individual privacy to initiate the study of attribute privacy, where a data owner is concerned about revealing sensitive properties of a whole dataset during analysis. We propose definitions to capture attribute privacy in two relevant cases where global attributes may need to be protected: (1) properties of a specific dataset and (2) parameters of the underlying distribution from which the dataset is sampled. We also provide two efficient mechanisms and one inefficient mechanism that satisfy attribute privacy for these settings. We base our results on a novel use of the Pufferfish framework to account for correlations across attributes in the data, thus addressing "the challenging problem of developing Pufferfish instantiations and algorithms for general aggregate secrets" that was left open by [KM14]. The paper is available online at: https://arxiv.org/abs/2009.04013

Bio:

Dr. Rachel Cummings is an Assistant Professor of Industrial Engineering and Operations Research at Columbia University. She was formerly an Assistant Professor of Industrial and Systems Engineering and (by courtesy) Computer Science at Georgia Tech. Her research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and information theory. Her work has focused on problems such as strategic aspects of data generation, incentivizing truthful reporting of data, privacy-preserving algorithm design, impacts of privacy policy, and human decision-making. Dr. Cummings received her Ph.D. in Computing and Mathematical Sciences from the California Institute of Technology, her M.S. in Computer Science from Northwestern University, and her B.A. in Mathematics and Economics from the University of Southern California.  She is the recipient of an NSF CAREER award, Apple Privacy-Preserving Machine Learning Award, JP Morgan Chase Faculty Award, a Google Research Fellowship for the Simons Institute program on Data Privacy, a Mozilla Research Grant, the ACM SIGecom Doctoral Dissertation Honorable Mention, the Amori Doctoral Prize in Computing and Mathematical Sciences, a Caltech Leadership Award, a Simons Award for Graduate Students in Theoretical Computer Science, and the Best Paper Award at the 2014 International Symposium on Distributed Computing.   Dr. Cummings also serves on the ACM U.S. Public Policy Council's Privacy Committee.

See rest of semester schedule at:

https://viterbi-web.usc.edu/~cstheory/theory-lunch.html