microsoft/SEAL: papers linking to it

GitHub · 4,065 stars · 779 forks · C++ · license MIT · created 2018 · github.com/microsoft/SEAL

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to microsoft/SEAL (https://github.com/microsoft/SEAL), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

5 papers in the tracked journals link to the GitHub repository microsoft/SEAL in their Crossref records, the first in 2021; most of them (2) in 2026.
By category: AI (5); the journals with the most are Expert Systems with Applications (2), Applied Intelligence (1) and Neural Computing and Applications (1).
2 of the papers build on one another: a later paper cites an earlier one that also links microsoft/SEAL.

By year#

20212026

Papers linking to microsoft/SEAL by publication year
Year	Papers
2021	1
2024	1
2025	1
2026	2

Journals#

Journals whose papers link to microsoft/SEAL most
Journal	Papers
Expert Systems with Applications	2
Applied Intelligence	1
Neural Computing and Applications	1
Knowledge and Information Systems	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Support vector machine classification over encrypted data Applied Intelligence, 2021
- EVAD: encrypted vibrational anomaly detection with homomorphic encryption Neural Computing and Applications, 2024 — cites the paper above

Papers linking to microsoft/SEAL, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-08-25	Support vector machine classification over encrypted data	Applied Intelligence	Hai Huang, Yongjian Wang, Haoran Zong	26	cites it
2024-03-01	EVAD: encrypted vibrational anomaly detection with homomorphic encryption	Neural Computing and Applications	Alessandro Falcetta, Manuel Roveri	11	cites it
2025-11-17	Preserving data and model privacy during inference and training	Expert Systems with Applications	William Briguglio, Issa Traoré, Mohammad Mamun et al.	0	cites it
2026-09-01	Privacy-preserving methodologies against privacy attacks on deep learning: a survey	Knowledge and Information Systems	Subhasish Ghosh, Amit Kr Mandal	0	cites it
2026-10-01	ARION: Attention-Optimized Transformer Inference on Encrypted Data	Expert Systems with Applications	Linhan Yang, Jingwei Chen, Wangchen Dai et al.	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). microsoft/SEAL: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-microsoft-seal

@misc{scholar4ai-code-github-microsoft-seal,
  author = {{Scholar4AI}},
  title = {microsoft/SEAL: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-microsoft-seal}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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