[Thanks to Joel Reidenberg for encouraging this deeper dive into news headlines!] There is no guarantee that a news headline you see online today will not change tomorrow, or even in the next hour, or will even be the same headlines your neighbor sees right now. For a real-life example of the type of change […]
All the News That’s Fit to Change: Insights into a corpus of 2.5 million news headlines
September 14, 2016 by
Improving Bitcoin’s Privacy and Scalability with TumbleBit
September 13, 2016 by
Last week we unveiled TumbleBit, a new anonymous payments scheme that addresses two major technical challenges faced by Bitcoin today: (1) scaling Bitcoin to meet increasing use, and (2) protecting the privacy of payments made via Bitcoin. Our proof-of-concept source code and a pre-print of the latest version of our paper were both posted online […]
Routing Detours: Can We Avoid Nation-State Surveillance?
August 30, 2016 by
Since 2013, Brazil has taken significant steps to build out their networking infrastructure to thwart nation-state mass surveillance. For example, the country is deploying a 3,500-mile fiber cable from Fortaleza, Brazil to Portugal; they’ve switched their government email system from Microsoft Outlook to a state-built system called Expresso; and they now have the largest IXP […]
Differential Privacy is Vulnerable to Correlated Data — Introducing Dependent Differential Privacy
August 26, 2016 by
[This post is joint work with Princeton graduate student Changchang Liu and IBM researcher Supriyo Chakraborty. See our paper for full details. — Prateek Mittal ] The tussle between data utility and data privacy Information sharing is important for realizing the vision of a data-driven customization of our environment. Data that were earlier locked up […]
Language necessarily contains human biases, and so will machines trained on language corpora
August 24, 2016 by
I have a new draft paper with Aylin Caliskan-Islam and Joanna Bryson titled Semantics derived automatically from language corpora necessarily contain human biases. We show empirically that natural language necessarily contains human biases, and the paradigm of training machine learning on language corpora means that AI will inevitably imbibe these biases as well. Specifically, we look at […]
