Differentially private mechanisms are increasingly used to publish tables of counts, where each entry represents the number of individuals belonging to a particular category. A distribution of counts summarizes the information in the count column, unlinking counts from categories. This object is useful for answering a class of research questions, but it is subject to statistical biases when counts are privatized with standard mechanisms. This motivates a novel design criterion we term accuracy of distribution. This study formalizes a two-stage framework for privatizing tables of counts that balances accuracy of distribution with two standard criteria of accuracy of counts and runtime. In the first stage, a distribution privatizer generates an estimate for the true distribution of counts. We introduce a new mechanism, called the cyclic Laplace, specifically tailored to distributions of counts, that outperforms existing general-purpose differentially private histogram mechanisms. In the second stage, a constructor algorithm generates a count mechanism, represented as a transition matrix, whose fixed-point is the privatized distribution of counts. We develop a mathematical theory that describes such transition matrices in terms of simple building blocks we call epsilon-scales. This theory informs the design of a new constructor algorithm that generates transition matrices with favorable properties more efficiently than standard optimization algorithms. We explore the practicality of our framework with a set of experiments, highlighting situations in which a fixed-point method provides a favorable tradeoff among performance criteria.

We formalize a notion of a privacy wrapper, defined as an algorithm that can take an arbitrary and untrusted script and produce an output with differential privacy guarantees. Our novel privacy wrapper, named TAHOE, incorporates two design ideas: a type of stability under subsetting, and randomization over subset size. We show that TAHOE imposes differential privacy for every possible script. When the data alphabet is finite and small enough, TAHOE can be practically run on a single computer. Performance simulations show that TAHOE has greater accuracy than a benchmark algorithm based on a subsample-and-aggregate approach for certain scenarios and parameter values.

Word clouds continue to be a popular tool for summarizing textual information, despite their well-documented deficiencies for analytic tasks. Much of their popularity rests on their playful visual appeal. In this paper, we present the results of a series of controlled experiments that show that layouts in which words are arranged into semantically and visually distinct zones are more effective for understanding the underlying topics than standard word cloud layouts. White space separators and/or spatially grouped color coding led to significantly stronger understanding of the underlying topics compared to a standard Wordle layout, while simultaneously scoring higher on measures of aesthetic appeal. This work is an advance on prior research on semantic layouts for word clouds because that prior work has either not ensured that the different semantic groupings are visually or semantically distinct, or has not performed usability studies. An additional contribution of this work is the development of a dataset for a semantic category identification task that can be used for replication of these results or future evaluations of word cloud designs.

This study analyzes the use of paper exams in college-level STEM courses. It leverages a unique dataset of nearly 1,800 exams, which were scanned into a web application, then processed by a team of annotators to yield a detailed snapshot of the way instructors currently structure exams. The focus of the investigation is on the variety of question formats, and how they are applied across different course topics.

In an instructional setting it can be difficult to accurately assess the quality of information visualizations of several variables. Instead of a standard design critique, an alternative is to ask potential readers of the chart to answer questions about it. A controlled study with 47 participants shows a good correlation between aggregated novice heuristic evaluation scores and results of answering questions about the data, suggesting that the two forms of assessment can be complementary. Using both metrics in parallel can yield further benefits; discrepancies between them may reveal incorrect application of heuristics or other issues.

Today's Internet industry suffers from several well-known pathologies, but none is as destructive in the long term as its resistance to evolution. Rather than introducing new services, ISPs are presently moving towards greater commoditization. It is apparent that the network's primitive system of contracts does not align incentives properly. In this study, we identify the network's lack of accountability as a fundamental obstacle to correcting this problem: Employing an economic model, we argue that optimal routes and innovation are impossible unless new monitoring capability is introduced and incorporated with the contracting system. Furthermore, we derive the minimum requirements a monitoring system must meet to support first-best routing and innovation characteristics. Our work does not constitute a new protocol; rather, we provide practical and specific guidance for the design of monitoring systems, as well as a theoretical framework to explore the factors that influence innovation.