Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data
This map shows the geographic impact of Adam Smith's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Adam Smith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam Smith more than expected).
This network shows the impact of papers produced by Adam Smith. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Adam Smith. The network helps show where Adam Smith may publish in the future.
Co-authorship network of co-authors of Adam Smith
This figure shows the co-authorship network connecting the top 25 collaborators of Adam Smith.
A scholar is included among the top collaborators of Adam Smith based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Adam Smith. Adam Smith is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Smith, Adam, et al.. (2018). Distributed Differential Privacy via Mixnets.. arXiv (Cornell University).3 indexed citations
8.
Wang, Di, Adam Smith, & Jinhui Xu. (2018). Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation.. arXiv (Cornell University).3 indexed citations
9.
Ullman, Jonathan, Adam Smith, Kobbi Nissim, Uri Stemmer, & Thomas Steinke. (2018). The Limits of Post-Selection Generalization. Neural Information Processing Systems. 31. 6400–6409.2 indexed citations
10.
Thakurta, Abhradeep & Adam Smith. (2013). (Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings. Neural Information Processing Systems. 26. 2733–2741.38 indexed citations
11.
Thakurta, Abhradeep & Adam Smith. (2013). Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso. Conference on Learning Theory. 30. 819–850.49 indexed citations
12.
Kifer, Daniel, Adam Smith, & Abhradeep Thakurta. (2012). Private Convex Empirical Risk Minimization and High-dimensional Regression. Journal of Machine Learning Research. 23.75 indexed citations
13.
Kifer, Daniel, Adam Smith, & Abhradeep Thakurta. (2012). Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression.. Conference on Learning Theory.23 indexed citations
Raskhodnikova, Sofya & Adam Smith. (2006). A Note on Adaptivity in Testing Properties of Bounded Degree Graphs. Electronic colloquium on computational complexity. 13.10 indexed citations
17.
Raskhodnikova, Sofya, Dana Ron, Ronitt Rubinfeld, Amir Shpilka, & Adam Smith. (2005). Sublinear Algorithms for Approximating String Compressibility and the Distribution Support Size. Electronic colloquium on computational complexity.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.