Category: Privacy & Security
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Do Mobile News Alerts Undermine Media’s Role in Democracy? Madelyn Sanfilippo at CITP
Why do different people sometimes get different articles about the same event, sometimes from the same news provider? What might that mean for democracy? Speaking at CITP today is Dr. Madelyn Rose Sanfilippo, a postdoctoral research associate here at CITP. Madelyn empirically studies the governance of sociotechnical systems, as well as outcomes, inequality, and consequences…
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The Third Workshop on Technology and Consumer Protection
Arvind Narayanan and I are pleased to announce that the Workshop on Technology and Consumer Protection (ConPro ’19) will return for a third year! The workshop will once again be co-located with the IEEE Symposium on Security and Privacy, occurring in May 2019. ConPro is a forum for a diverse range of computer science research…
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User Perceptions of Smart Home Internet of Things (IoT) Privacy
by Noah Apthorpe This post summarizes a research paper, authored by Serena Zheng, Noah Apthorpe, Marshini Chetty, and Nick Feamster from Princeton University, which is available here. The paper will be presented at the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) on November 6, 2018. Smart home Internet of Things (IoT) devices…
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Disaster Information Flows: A Privacy Disaster?
By Madelyn R. Sanfilippo and Yan Shvartzshnaider Last week, the test of the Presidential Alert system, which many objected to on partisan grounds, brought the Wireless Emergency Alert system (WEA) into renewed public scrutiny. WEA, which distributes mobile push notifications about various emergencies, crises, natural disasters, and amber alerts based on geographic relevance, became operational…
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Building Respectful Products using Crypto: Lea Kissner at CITP
How can we build respect into products and systems? What role does cryptography play in respectful design? Speaking today at CITP is Lea Kissner (@LeaKissner), global lead of Privacy Technology at Google. Lea has spent the last 11 years designing and building security and privacy for Google projects from the grittiest layers of infrastructure to…
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PrivaCI Challenge: Context Matters
by Yan Shvartzshnaider and Marshini Chetty In this post, we describe the Privacy through Contextual Integrity (PrivaCI) challenge that took place as part of the symposium on applications of contextual integrity sponsored by Center for Information Technology Policy and Digital Life Initiative at Princeton University. We summarize the key takeaways from the unfolded discussion. We welcome…
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How can we scale private, smart contracts? Ed Felten on Arbitrum
Smart contracts are powerful virtual referees for holding money and carrying out agreed-on procedures in cases of disputes, but they can’t guarantee privacy and have strict scalability limitations. How can we improve on these constraints? Here at the Center for IT Policy, it’s the first event of our weekly Tuesday lunch series. Speaking today is…
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Thoughts on California’s Proposed Connected Device Privacy Bill (SB-327)
This post was authored by Noah Apthorpe. On September 6, 2018, the California Legislature presented draft legislation to Governor Brown regarding security and authentication of Internet-connected devices. This legislation would extend California’s existing reasonable data security requirement—which already applies to online services—to Internet-connected devices. The intention of this legislation to prevent default passwords and…
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Privacy, ethics, and data access: A case study of the Fragile Families Challenge
This blog post summarizes a paper describing the privacy and ethics process by which we organized the Fragile Families Challenge. The paper will appear in a special issue of the journal Socius. Academic researchers, companies, and governments holding data face a fundamental tension between risk to respondents and benefits to science. On one hand, these…
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What Are Machine Learning Models Hiding?
Machine learning is eating the world. The abundance of training data has helped ML achieve amazing results for object recognition, natural language processing, predictive analytics, and all manner of other tasks. Much of this training data is very sensitive, including personal photos, search queries, location traces, and health-care records. In a recent series of papers,…