In early 2021, the US Census Bureau will begin releasing statistical tables based on the decennial census conducted in 2020. Because of significant changes in the data landscape, the Census Bureau is changing its approach to disclosure avoidance. The confidentiality of individuals represented "anonymously" in these statistical tables will be protected by a "formal privacy" technique that allows the Bureau to mathematically assess the risk of revealing information about individuals in the released statistical tables. The Bureau's approach is an implementation of "differential privacy," and it gives a rigorously demonstrated guaranteed level of privacy protection that traditional methods of disclosure avoidance do not. Given the importance of the Census Bureau's statistical tables to democracy, resource allocation, justice, and research, confusion about what differential privacy is and how it might alter or eliminate data products has rippled through the community of its data users, namely: demographers, statisticians, and census advocates. The purpose of this primer is to provide context to the Census Bureau's decision to use a technique based on differential privacy and to help data users and other census advocates who are struggling to understand what this mathematical tool is, why it matters, and how it will affect the Bureau's data products.
Fish, Benjamin, Bashardoust, Ashkan, boyd, danah, Friedler, Sorelle A., Scheidegger, Carlos, and Venkatasubramanian, Suresh
Computer Science - Social and Information Networks and Physics - Physics and Society
The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individuals may place a high value on receiving information, e.g. job openings or advertisements for loans. While well-connected individuals at the center of the network are likely to receive the information that is being distributed through the network, poorly connected individuals are systematically less likely to receive the information, producing a gap in access to the information between individuals. In this work, we study how best to spread information in a social network while minimizing this access gap. We propose to use the maximin social welfare function as an objective function, where we maximize the minimum probability of receiving the information under an intervention. We prove that in this setting this welfare function constrains the access gap whereas maximizing the expected number of nodes reached does not. We also investigate the difficulties of using the maximin, and present hardness results and analysis for standard greedy strategies. Finally, we investigate practical ways of optimizing for the maximin, and give empirical evidence that a simple greedy-based strategy works well in practice. Comment: Accepted at The Web Conference 2019
An interview with author Danah Zohar is presented. Topics discussed include "The Quantum Self," the development of quantum physics, and quantum physicist David Bohm. Also mentioned are the importance of leadership management and the relationship among spiritual intelligence (SQ), emotional intelligence (EQ), and cognitive intelligence (IQ).
This study presents a numerical procedure, which we call the macroscopic forcing method (MFM), which reveals the differential operators acting upon the mean fields of quantities transported by underlying fluctuating flows. Specifically, MFM can precisely determine the eddy diffusivity operator, or more broadly said, it can reveal differential operators associated with turbulence closure for scalar and momentum transport. We present this methodology by considering canonical problems with increasing complexity. As an example demonstrating the usefulness of the developed methodology, we show that an eddy diffusivity operator, i.e. model form, obtained from an MFM analysis of homogeneous isotropic turbulence leads to significant improvement in RANS prediction of axisymmetric turbulent jets. We show a cost-effective generalization of MFM for analysis of non-homogeneous and wall-bounded flows, where the eddy diffusivity is found to be a convolution acting on the macroscopic gradient of transported quantities. We introduce MFM as an effective tool for quantitative understanding of non-Boussinesq effects and assessment of model forms in turbulence closures, particularly, the effects associated with anisotropy and non-locality of macroscopic mixing. Comment: 50 pages, 10 figures