Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy

By House Report. 2013

254 citations recorded by the source corpus.

Trust is essential to maintaining the social and economic benefits that networked technologies bring to the United States and the rest of the world. With the confidence that companies will handle information about them fairly and responsibly, consumers have turned to the Internet to express their creativity, join political movements, form and maintain friendships, and engage in commerce. The Internet’s global connectivity means that a single innovator’s idea can grow rapidly into a product or service that becomes a daily necessity for hundreds of millions of consumers. American companies lead the way inproviding these technologies, and the United States benefits through job creation and economic growth as a result. Our continuing leadership in this area depends on American companies’ ability to earn and maintain the trust of consumers in a global marketplace.

Subfields: Sociology and Political Science, Privacy & Security, AI Ethics, Fairness & Society

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Citations in the corpus

Federated Learning for Mobile Keyboard Prediction (2018, methodology): “Following the principle of data minimization [25], uploaded content is limited to model weights.”
Communication-Efficient Learning of Deep Networks from Decentralized Data (2016, background): “This is a direct application of the principle of focused collection or data minimization proposed by the 2012 White House report on privacy of consumer data [39].”
Federated Optimization: Distributed Machine Learning for On-Device Intelligence (2016, background): “This is a direct application of the principle of focused collection or data minimization proposed by the 2012 White House report on the privacy of consumer data [98].”
Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals (2016, background): “The conclusion that cloaking may be more or less difficult based on the number of features (Likes) that one would need to cloak is based on several assumptions.”
Counteracting the Negative Effect of Form Auto-completion on the Privacy Calculus (2013, background): “White therefore argues that “Marketers’ efforts may be wisely directed at attempts to mitigate any perceived “downside risks” associated with disclosure.””
Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation (2019, background): “In order to maintain the privacy of consumer data [4] and reduce the communication cost, it is necessary to have an emergence of a new class of machine learning techniques that shifts the computation to the edge network where the privacy of data is maintained.”
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning (2018, methodology): “, user-generated photos on edge devices), Federated Learning [54] provides a solution based on the principles of data minimization [43, 60] towards training a ML model.”
Federated Learning over Wireless Networks: Optimization Model Design and Analysis (2019, background): “The rise in interest in maintaining the privacy of consumer data [1] has led to the emergence of a new class of machine learning techniques that exploit the participation of a number of mobile phone users.”
Federated Learning of Deep Networks using Model Averaging (2016, methodology): “This is a direct application of the principle of focused collection or data minimization as outlined in the Consumer Privacy Bill of Rights (White House Report, 2013).”
A Design Space for Effective Privacy Notices (2015, background): “Multistakeholder processes have been initiated in the wake of the White House’s proposed Consumer Bill of Rights [122] to tackle transparency and control issues of mobile privacy [92] and facial recognition [93].”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, methodology): “Data minimization [40], [71], [14] is a fundamental legal instrument that protects privacy by limiting the collection of”
The limits of transparency: Data brokers and commodification (2018, background): “Along these lines, the Obama administration’s proposed Consumer Privacy Bill of Rights seeks to implement degrees of contextual integrity by limiting the use of consumer data to expressly stated or otherwise “reasonable” purposes (White House, 2012).”
CMFL: Mitigating Communication Overhead for Federated Learning (2019, background): “Instead of uploading the privacy-sensitive training data directly to the central server, the participating clients in Federated Learning only send the model updates which could be ephemeral [2], [5], [14]–[16].”
The Economics of Privacy (2016, background): “The White House Privacy White Paper, released in February 2012 (White House, 2012), and concurrent announcements by Internet giants such as Google Inc. (Angwin, 2012), supporting a do not track mechanism, seem to make such a system a realistic expectation in the near future.”