Demo Data from EDBT 2026 / reviewers in the wild / expert
Shiladitya De
dblp:409/8721
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0005-4234-472XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 2 since 2021
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.
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 62% Software maintenance and evolution · 38% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › software ecosystems
npm packages |
0.3 | 1 | 2025 | "I wasn't sure if this is indeed a security risk": Data-driven Understanding of Security Issue Reporting in GitHub Repositories of Open Source npm Packages · USENIX Security Symposium 2025 |
Software maintenance and evolution
software ecosystems |
0.3 | 1 | 2025 | "I wasn't sure if this is indeed a security risk": Data-driven Understanding of Security Issue Reporting in GitHub Repositories of Open Source npm Packages · USENIX Security Symposium 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | "To Pay or Not to Pay?": Understanding User Decision-Making and Influence of Nudges in UPI AppsabstractIn response to increasing social engineering attacks, Unified Payments Interface (UPI) apps have implemented a variety of nudges to deter users from responding to fraudulent payment requests. We conducted a scenario-based semi-structured interview study with 46 Indian participants who tested the impact of various user interface nudges to help them discern a mixture of fraudulent and non-fraudulent payment scenarios. Our study revealed a multi-stage de cision-making process: participants relied on a digital literacy and security concern-based risk assessment, which is further influenced by their trust in the recipient as well as the perceived need of the financial transaction. Our results demonstrate that while most nudges help to combat fraudulent transactions, participants perceived “badges” (a nudge signaling the trustworthiness of the receiver) to be most effective in combating fraud. We conclude with concrete recommendations for current and future UPI developers. Nandini Bajaj, Shiladitya De, Kshitiz Sharma, Xinru Page, Bart P. Knijnenburg, Mainack Mondal |
AsiaCCS | 2 |
| 2025 | "I wasn't sure if this is indeed a security risk": Data-driven Understanding of Security Issue Reporting in GitHub Repositories of Open Source npm Packages
Rajdeep Ghosh 0005, Shiladitya De, Mainack Mondal |
USENIX Security Symposium | 2 |