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Aarushi Dubey

dblp:257/8701 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0001-6109-240XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSecurity and privacy · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-authorTheory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Privacy and data protection · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Privacy and data protection
privacy analysis		 1.012026
The Role of Privacy Guarantees in Voluntary Donation of Private Health Data for Altruistic Goals · NDSS 2026
Medical and health informatics › clinical informatics
health data sharing		 0.312026
The Role of Privacy Guarantees in Voluntary Donation of Private Health Data for Altruistic Goals · NDSS 2026
YearPublicationVenuePosition
2026 The Role of Privacy Guarantees in Voluntary Donation of Private Health Data for Altruistic Goals
Ruizhe Wang 0003, Roberta De Viti, Aarushi Dubey, Elissa M. Redmiles
 NDSS3
2019 Using Machine Learning to Predict High School Student Employability - A Case Study
abstract
In this paper, we explore the use of supervised machine learning models to predict the employability of high school students with local businesses for part-time jobs. We further compare the performance of trained models used in this analysis to one another. Empirical results show that it is possible to predict the employability of high school students with local businesses with high-predictive accuracies. The trained predictive models perform better with larger dataset, with up to 93% accuracy.
Aarushi Dubey, Muthukumara Mani
 DSAA1