The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mutual Information Analysis
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Cited by
Introduction to differential power analysis
Kocher, P., Jaffe, J., Jun, B., & Rohatgi, P. (2011) · Journal of Cryptographic Engineering · doi:10.1007/s13389-011-0006-y · Cited by 632
Leakage Assessment Methodology: A Clear Roadmap for Side-Channel Evaluations
Schneider, T., & Moradi, A. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-48324-4_25 · Cited by 268
Microwalk: A framework for finding side channels in binaries
Wichelmann, J., Moghimi, A., Eisenbarth, T., & Sunar, B. (2018) · Proceedings of the 34th Annual Computer Security Applications Conference · doi:10.1145/3274694.3274741 · Cited by 74
Efficient and Side-Channel Resistant Ed25519 on ARM Cortex-M4
Owens, D., Khatib, R. E., Bisheh-Niasar, M., Azarderakhsh, R., & Kermani, M. M. (2024) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2024.3384414 · Cited by 58
Feature Augmentation with Reinforcement Learning
Liu, J., Chai, C., Luo, Y., Lou, Y., Feng, J., & Tang, N. (2022) · 2022 IEEE 38th International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00317 · Cited by 33
Metadata
- Authors
- Gierlichs, B., Batina, L., Tuyls, P., & Preneel, B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008-08-05
- Pages
- 426-442
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1862426464
- Cited by
- 626
- References
- 1
- FWCI
- 21.83
- Type
- conference paper
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Mutual Information Analysis. https://app.cochat.ai/papers/10.1007/978-3-540-85053-3_27
- The paper
- Gierlichs, B., Batina, L., Tuyls, P., & Preneel, B. (2008). Mutual Information Analysis. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-85053-3_27
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
