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  • All HBS Web  (32)
    • Faculty Publications  (10)

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    • All HBS Web  (32)
      • Faculty Publications  (10)

      Differential Privacy Remove Differential Privacy →

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      • 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
      Keywords: Machine Learning; AI and Machine Learning
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      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).
      • April 2021
      • 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
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      Yoffie, David B., and Daniel Fisher. "Social Media War 2021: Snap vs. Facebook vs. TikTok." Harvard Business School Case 721-443, April 2021.
      • 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
      Keywords: Differential Privacy; Mechanism Design; Finance; Mathematical Methods
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      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.
      • 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
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      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).
      • 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
      Keywords: Machine Learning; Algorithms; Objective Perturbation; Mathematical Methods
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      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).
      • 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
      Keywords: Machine Learning; Transfer Theorem; Mathematical Methods
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      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.
      • 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
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      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
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      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

      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
      Keywords: Differential Privacy; Empirical Risk Minimization; Accuracy First
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      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

      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
      Keywords: Bandit Algorithms; Bias; Analytics and Data Science; Mathematical Methods; Theory
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      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).
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