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.
HAIL
2009477 citationsKevin D. Bowers, Ari Juels et al.profile →
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
2018456 citationsMatthew Jagielski, Alina Oprea et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Alina Oprea'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 Alina Oprea with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alina Oprea more than expected).
This network shows the impact of papers produced by Alina Oprea. 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 Alina Oprea. The network helps show where Alina Oprea may publish in the future.
Co-authorship network of co-authors of Alina Oprea
This figure shows the co-authorship network connecting the top 25 collaborators of Alina Oprea.
A scholar is included among the top collaborators of Alina Oprea 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 Alina Oprea. Alina Oprea is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Jagielski, Matthew, Jonathan Ullman, & Alina Oprea. (2020). Auditing Differentially Private Machine Learning: How Private is Private SGD?. Neural Information Processing Systems. 33. 22205–22216.3 indexed citations
12.
Lauinger, Tobias, et al.. (2020). What's in an Exploit? An Empirical Analysis of Reflected Server XSS Exploitation Techniques.. 107–120.3 indexed citations
13.
Oprea, Alina, et al.. (2019). Playing Adaptively Against Stealthy Opponents: A Reinforcement Learning Strategy for the FlipIt Security Game.. arXiv (Cornell University).
14.
Demontis, Ambra, Marco Melis, Maura Pintor, et al.. (2018). On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks.. arXiv (Cornell University).2 indexed citations
15.
Dijk, Marten van, Ari Juels, Alina Oprea, & Ronald L. Rivest. (2012). FlipIt: The Game of “Stealthy Takeover”. Journal of Cryptology. 26(4). 655–713.150 indexed citations
16.
Bowers, Kevin D., Marten van Dijk, Ari Juels, Alina Oprea, & Ronald L. Rivest. (2010). How to Tell if Your Cloud Files Are Vulnerable to Drive Crashes.. DSpace@MIT (Massachusetts Institute of Technology). 2010. 214.2 indexed citations
17.
Bowers, Kevin D., Ari Juels, & Alina Oprea. (2009). Proofs of retrievability. 43–54.257 indexed citations
18.
Oprea, Alina & Michael K. Reiter. (2007). Integrity checking in cryptographic file systems with constant trusted storage. USENIX Security Symposium. 13.21 indexed citations
19.
Oprea, Alina & Michael K. Reiter. (2005). Space-Efficient Block Storage Integrity.. Network and Distributed System Security Symposium.46 indexed citations
20.
Kissner, Lea, Alina Oprea, Michael K. Reiter, Dunlun Song, & Ke Yang. (2004). Private Keyword-Based Push and Pull with Applications to Anonymous Communication (Extended Abstract).
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.