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- Faculty Publications (12)
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- Article
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
By: Katrina Ligett, Seth Neel, Aaron Leon Roth, Bo Waggoner and Steven Wu
Traditional approaches to differential privacy assume a fixed privacy requirement ϵ for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint. As differential privacy is increasingly deployed in practical settings, it... View Details
Ligett, Katrina, Seth Neel, Aaron Leon Roth, Bo Waggoner, and Steven Wu. "Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM." Journal of Privacy and Confidentiality 9, no. 2 (2019).
- Article
How to Use Heuristics for Differential Privacy
By: Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
We develop theory for using heuristics to solve computationally hard problems in differential privacy. Heuristic approaches have enjoyed tremendous success in machine learning, for which performance can be empirically evaluated. However, privacy guarantees cannot be... View Details
Neel, Seth, Aaron Leon Roth, and Zhiwei Steven Wu. "How to Use Heuristics for Differential Privacy." Proceedings of the IEEE Annual Symposium on Foundations of Computer Science (FOCS) 60th (2019).
- Article
The Role of Interactivity in Local Differential Privacy
By: Matthew Joseph, Jieming Mao, Seth Neel and Aaron Leon Roth
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to... View Details
Joseph, Matthew, Jieming Mao, Seth Neel, and Aaron Leon Roth. "The Role of Interactivity in Local Differential Privacy." Proceedings of the IEEE Annual Symposium on Foundations of Computer Science (FOCS) 60th (2019).
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
A New Analysis of Differential Privacy’s Generalization Guarantees
We give a new proof of the “transfer theorem” underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and... View Details
- Oct 2020
- Conference Presentation
Optimal, Truthful, and Private Securities Lending
By: Emily Diana, Michael J. Kearns, Seth Neel and Aaron Leon Roth
We consider a fundamental dynamic allocation problem motivated by the problem of securities lending in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource... View Details
Diana, Emily, Michael J. Kearns, Seth Neel, and Aaron Leon Roth. "Optimal, Truthful, and Private Securities Lending." Paper presented at the 1st Association for Computing Machinery (ACM) International Conference on AI in Finance (ICAIF), October 2020.
- Mar 2020
- Conference Presentation
A New Analysis of Differential Privacy's Generalization Guarantees
By: Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and... View Details
Jung, Christopher, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld. "A New Analysis of Differential Privacy's Generalization Guarantees." Paper presented at the 11th Innovations in Theoretical Computer Science Conference, Seattle, March 2020.
- 19 May 2014
- Research & Ideas
Why Companies Should Compete for Your Privacy
Consumers are increasingly wary about sharing personal information with firms. Yet when they benefit from providing information in exchange for lower prices or better services, many consumers will gladly make the privacy trade-off. But... View Details
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which... View Details
Huang, Ta-Wei, and Eva Ascarza. "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach." Harvard Business School Working Paper, No. 24-034, December 2023.
- April 2021 (Revised March 2024)
- Case
Social Media War 2021: Snap vs. Facebook vs. TikTok
By: David B. Yoffie and Daniel Fisher
This case explores the competitive war between Snap, Facebook, and TikTok in 2021. The strategic focus is on Snapchat: how should it respond to the emergence of TikTok, and how should it compete with the dominant competitor in its space—Facebook. The case examines... View Details
Keywords: Strategy Development; Competitor Analysis; Strategy; Network Effects; Competitive Strategy; Decision Choices and Conditions; Social Media
Yoffie, David B., and Daniel Fisher. "Social Media War 2021: Snap vs. Facebook vs. TikTok." Harvard Business School Case 721-443, April 2021. (Revised March 2024.)
- Article
Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it.... View Details
Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- Article
Adaptive Machine Unlearning
By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details
Gupta, Varun, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. "Adaptive Machine Unlearning." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 08 May 2007
- First Look
First Look: May 8, 2007
occur relatively quickly when the underlying repugnance changes. Download the paper: http://www.hbs.edu/research/pdf/07-077.pdf An Empirical Approach to Understanding Privacy Valuation Authors:Luc Wathieu and Allan Friedman Abstract The... View Details
Keywords: Martha Lagace
- Article
Assessing the Food and Drug Administration's Risk-Based Framework for Software Precertification with Top Health Apps in the United States: Quality Improvement Study
By: Noy Alon, Ariel Dora Stern and John Torous
BACKGROUND: As the development of mobile health apps continues to accelerate, the need to implement a framework that can standardize categorizing these apps to allow for efficient, yet robust regulation grows. However, regulators and researchers are faced with numerous... View Details
Keywords: Mobile Health; Smartphone; Food And Drug Administration; Risk-based Framework; Health Care and Treatment; Mobile and Wireless Technology; Applications and Software; Framework
Alon, Noy, Ariel Dora Stern, and John Torous. "Assessing the Food and Drug Administration's Risk-Based Framework for Software Precertification with Top Health Apps in the United States: Quality Improvement Study." JMIR mHealth and uHealth 8, no. 10 (October 2020).
- 22 Feb 2024
- Research & Ideas
How to Make AI 'Forget' All the Private Data It Shouldn't Have
Europe’s tougher data privacy regulations went into effect in 2018, they have created complications for companies worldwide. Questions around data privacy will likely become thornier as generative artificial... View Details
- Web
Live from Klarman Hall - Alumni
including visualization and copyrighting, hyper-personalization, media planning and buying, and general efficiency and organizational knowledge. The session will delve into how AI-driven strategies are revolutionizing job structures and marketing efficiencies,... View Details
- 09 Oct 2018
- First Look
New Research and Ideas, October 9, 2018
process. Can algorithms, machine learning, and artificial intelligence help Tailor Brands outperform graphic designers and branding agencies in developing brand identities? And, can Tailor Brands differentiate itself from the many other... View Details
Keywords: Dina Gerdeman
- 31 Jan 2022
- Research & Ideas
Where Can Digital Transformation Take You? Insights from 1,700 Leaders
privacy and security. Social responsibility in the broadest sense, roundtable participants said, has become a competitive “must,” essential for attracting talent and building trust with customers. Six qualities of digitally mature... View Details
- 05 Feb 2013
- First Look
First Look: Feb. 5
blended identity; thus, limiting the extent to which such organizations can truly "re-direct" future career choices. Strategic Orientations in a Competitive Context: The Role of Strategic Orientation Differentiation... View Details
Keywords: Sean Silverthorne
- 01 Sep 2023
- News
Money Does Grow on (Family) Trees
disclose this to people who take the test.” Despite the potential for uprooting one’s expected family tree and the privacy and ethics concerns surrounding sharing one’s genetic code with for-profit companies, DNA testing proved popular... View Details