Rachel Cummings

Assistant Professor, Industrial and Systems Engineering Georgia Tech College of Engineering

  • Atlanta GA

Rachel Cummings is an expert in data privacy, algorithmic economics, optimization, statistics, and information theory.

Contact

Georgia Tech College of Engineering

View more experts managed by Georgia Tech College of Engineering

Spotlight

2 min

Locking down your data. Are lawmakers finally waking up to the importance of privacy?

Data collection and data control are becoming international issues. As the lucrative and important pieces of customer data collection become a priority for major tech and software companies – privacy and protection is now emerging as the key issue for international legislators. Just recently, Microsoft had to update several of its agreements with cloud customers and re-classify its role in Europe. Last month, as part of an enquiry that opened earlier this year, the European Data Protection Supervisor (EDPS) expressed 'serious concerns' over whether the relevant contractual terms were compliant with GDPR, and over Microsoft's role as a data processor or data controller for EU institutions. The report followed the publication of a series of papers by the Dutch Ministry of Justice and Security, suggesting that Office 365 was breaching GDPR by collecting 'functional and diagnostics data', including email subject lines and text run through a spell-checker. Microsoft has now acknowledged its position as a data controller which has a higher bar for ensuring user data when it comes to the provision of enterprise services. "In the [Online Services Terms] OST update, we will clarify that Microsoft assumes the role of data controller when we process data for specified administrative and operational purposes incident to providing the cloud services covered by this contractual framework, such as Azure, Office 365, Dynamics and Intune," says Julie Brill, Microsoft's corporate vice president for global privacy and regulatory affairs and chief privacy officer. "This subset of data processing serves administrative or operational purposes such as account management; financial reporting; combating cyber attacks on any Microsoft product or service; and complying with our legal obligations."  Forbes Magazine – November 18 Data collection and control are becoming big issues on a global scale as more and more governments are looking for consumer protection while companies are seeking the profit that comes from the information customers provide voluntarily and sometimes unwillingly . Are you a reporter covering technology, privacy and data collection and control?  Did you know that there is value in the results of spell-checkers and document review tools? If you have questions or need an expert source for insight and perspective – let us help. Dr. Rachel Cummings is an expert in data privacy, algorithmic economics, optimization, statistics, and information theory. Dr. Cummings is available to speak with media regarding data privacy and other topics, simply click on her icon to arrange an interview.

Rachel Cummings

Media

Social

Biography

Dr. Rachel Cummings is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering 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 a Google Research Fellowship, a Simons-Berkeley Research Fellowship in Data Privacy, 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.

Areas of Expertise

Medical Instrumentation
Home Monitoring of Chronic Disease
Data Generation
Machine Learning
Data Privacy
Algorithmic Economics
Statistics
Optimization
Non-invasive Physiological Monitoring
Cardiomechanical Signs

Selected Accomplishments

Google Research Fellowship

Google Research Fellowship Spring 2019

Simons-Berkeley Research Fellowship in Data Privacy

Simons-Berkeley Research Fellowship in Data Privacy Spring 2019

ACM SIGecom Doctoral Dissertation Honorable Mention

ACM SIGecom Doctoral Dissertation Honorable Mention 2018

Education

California Institute of Technology

Ph.D.

Computing and Mathematical Sciences

2017

Northwestern University

M.S.

Computer Science

2013

University of Southern California

B.A.

Mathematics, Economics

2011

Selected Articles

Differentially Private Online Submodular Minimization

The 22nd International Conference on Artificial Intelligence and Statistics

2019

In this paper we develop the first algorithms for online submodular minimization that preserve differential privacy under full information feedback and bandit feedback. Our first result is in the full information setting, where the algorithm can observe the entire function after making its decision at each time step. We give an algorithm in this setting that is $\eps $-differentially private and achieves expected regret $\tilde {O}\left (\frac {n\sqrt {T}}{\eps}\right) $ over rounds for a collection of elements. Our second result is in the bandit setting, where the algorithm can only observe the cost incurred by its chosen set, and does not have access to the entire function.

View more

On the Compatibility of Privacy and Fairness

Georgia Institute of Technology

2019

In this work, we investigate whether privacy and fairness can be simultaneously achieved by a single classifier in several different models. Some of the earliest work on fairness in algorithm design defined fairness as a guarantee of similar outputs for “similar” input data, a notion with tight technical connections to differential privacy. We study whether tensions exist between differential privacy and statistical notions of fairness, namely Equality of False Positives and Equality of False Negatives (EFP/EFN). We show that even under full distributional access, there are cases where the constraint of differential privacy precludes exact EFP/EFN. We then turn to ask whether one can learn a differentially private classifier which approximately satisfies EFP/EFN, and show the existence of a PAC learner which is private and approximately fair with high probability. We conclude by giving an efficient algorithm for classification that maintains utility and satisfies both privacy and approximate fairness with high probability.

View more

The Implications of Privacy-Aware Choice

California Institute of Technology

2017

Privacy concerns are becoming a major obstacle to using data in the way that we want. It's often unclear how current regulations should translate into technology, and the changing legal landscape surrounding privacy can cause valuable data to go unused. In addition, when people know that their current choices may have future consequences, they might modify their behavior to ensure that their data reveal less---or perhaps, more favorable---information about themselves. Given these concerns, how can we continue to make use of potentially sensitive data, while providing satisfactory privacy guarantees to the people whose data we are using? Answering this question requires an understanding of how people reason about their privacy and how privacy concerns affect behavior.

View more