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  • Research Summary

An Empirical Approach to Understanding Privacy Valuation

The purpose of this research is to detect the presence of sophisticated economic motives behind individual concerns for privacy. Recent theories of privacy demands in commercial contexts have assumed an economically aware and sophisticated consumer, capable of... View Details
  • 22 Apr 2013
  • Working Paper Summaries

Competing with Privacy

Keywords: by Ramon Casadesus-Masanell & Andres Hervas-Drane; Publishing
  • 19 May 2020
  • Research & Ideas

Why Privacy Protection Notices Turn Off Shoppers

make you feel less safe by making you conscious of just how much you are giving away? A new working paper finds that it’s more likely to be the latter. “Even if a privacy policy is meant to be assuaging, it can prime you to think about... View Details
Keywords: by Michael Blanding; Retail
  • 13 Feb 2024
  • Cold Call Podcast

Apple’s Dilemma: Balancing Privacy and Safety Responsibilities

Keywords: Re: Nien-he Hsieh & Henry W. McGee
  • 02 Mar 2016
  • What Do You Think?

Is Apple’s Real Privacy Challenge Technology Innovation Itself?

Is the Discussion of Infotech Security versus Privacy a Waste of Time? Summing Up Discussion of this month’s question devolved into a debate about the usual issues: privacy versus security, Apple versus the... View Details
Keywords: by James Heskett; Technology
  • 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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  • 13 Apr 2020
  • Working Paper Summaries

The Bulletproof Glass Effect: When Privacy Notices Backfire

Keywords: by Aaron R. Brough, David A. Norton, and Leslie John
  • 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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  • 2003
  • Chapter

Market Solutions to Privacy Problems?

By: J. A. Deighton
Keywords: Knowledge Management; Information Management; Markets; Rights
Related
  • Article

Facebook Knows It Can't Offer More Privacy

By: Scott Duke Kominers
Related
  • 2002
  • Working Paper

Market Solutions to Privacy Problems?

By: John Deighton
Related
  • October 1991 (Revised October 1993)
  • Case

Equifax: Privacy in the Information Age

By: Lynn S. Paine
Keywords: Credit; Ethics; Financial Services Industry
Related
  • 04 Jun 2024
  • Research & Ideas

Navigating Consumer Data Privacy in an AI World

Being proactive about how you handle data and run your models to protect privacy isn't just smart—it's essential. It’s really the best way to go about it. “There's a growing awareness among customers and users about how their data is... View Details
Keywords: by Rachel Layne; Technology; Information Technology
  • 2023
  • Working Paper

Data Governance, Interoperability and Standardization: Organizational Adaptation to Privacy Regulation

By: Sam (Ruiqing) Cao and Marco Iansiti
The increasing availability of data can afford dynamic competitive advantages among data-intensive corporations, but governance bottlenecks hinder data-driven value creation and increase regulatory risks. We analyze the role of two technological features of data... View Details
Keywords: Organizations; Information Technology; Performance Productivity; Growth and Development; Transformation
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  • September 2022 (Revised July 2023)
  • Case

Data Privacy in Practice at LinkedIn

By: Iavor Bojinov, Marco Iansiti and Seth Neel
Related
  • 2023
  • Article

MoPe: Model Perturbation-based Privacy Attacks on Language Models

By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
Keywords: Large Language Model; AI and Machine Learning; Cybersecurity
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  • October 2016 (Revised March 2018)
  • Teaching Note

Apple: Privacy vs. Safety? (A) and (B)

By: Nien-hê Hsieh, Henry McGee and Sarah McAra
Teaching Note for HBS No. 316-069. View Details
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  • March – April 1975
  • Article

Personal Privacy versus the Corporate Commuter

By: K. Goldstein and R. L. Nolan
Related
  • 2007
  • Working Paper

An Empirical Approach to Understanding Privacy Valuation

By: Luc Wathieu and Allan Friedman
Related
  • 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
Keywords: by Dina Gerdeman; Consumer Products

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