The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Big data security and privacy in healthcare: A Review
Open accessAbstract
The ever-increasing integration of highly diverse enabled data generating technologies in medical, biomedical and healthcare fields and the growing availability of data at the central location that can be used in need of any organization from pharmaceutical manufacturers to health insurance companies to hospitals have primarily make healthcare organizations and all its sub-sectors in face of a flood of big data as never before experienced. While this data is being hailed as the key to improving health outcomes, gain valuable insights and lowering costs, the security and privacy issues are so overwhelming that healthcare industry is unable to take full advantage of it with its current resources. Managing and harnessing the analytical power of big data, however, is vital to the success of all healthcare organizations. It is in this context that this paper aims to present the state-of-the-art security and privacy issues in big data as applied to healthcare industry and discuss some available data privacy, data security, users’ accessing mechanisms and strategies.
Overview
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
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
L-diversity: privacy beyond k-anonymity
Machanavajjhala, A., Gehrke, J., Kifer, D., & Venkitasubramaniam, M. (2006) · 22nd International Conference on Data Engineering (ICDE'06) · doi:10.1109/icde.2006.1 · Cited by 2,362
Protecting respondents identities in microdata release
Samarati, P. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.971193 · Cited by 2,324
ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s021848850200165x · Cited by 1,976
Samarati, P., & Sweeney, L. (2018) · doi:10.1184/r1/6625469 · Cited by 1,326
Mondrian Multidimensional K-Anonymity
LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2006) · 22nd International Conference on Data Engineering (ICDE'06) · doi:10.1109/icde.2006.101 · Cited by 1,124
Transforming data to satisfy privacy constraints
Iyengar, V. S. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775089 · Cited by 790
Security and privacy for storage and computation in cloud computing
Wei, L., Zhu, H., Cao, Z., Dong, X., Jia, W., Chen, Y., & Vasilakos, A. V. (2014) · Information Sciences · doi:10.1016/j.ins.2013.04.028 · Cited by 516
Big data privacy: a technological perspective and review
Jain, P., Gyanchandani, M., & Khare, N. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0059-y · Cited by 392
Cited by
Blockchain in healthcare applications: Research challenges and opportunities
McGhin, T., Choo, K.-K. R., Liu, C. Z., & He, D. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.02.027 · Cited by 866
An exhaustive survey on security and privacy issues in Healthcare 4.0
Hathaliya, J. J., & Tanwar, S. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.018 · Cited by 462
Bayor, A. A., Li, J., Yang, I. A., & Varnfield, M. (2025) · Journal of Medical Internet Research · doi:10.2196/63733 · Cited by 46
“While vast clinical datasets may be available, navigating access protocols can be cumbersome due to data access privacy concerns [85,86].”
Metadata
- Authors
- Abouelmehdi, K., Beni-Hssane, A., Khaloufi, H., & Saadi, M.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2017
- Volume
- 113
- Pages
- 73-80
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2756289758
- Cited by
- 264
- References
- 25
- FWCI
- 7.88
- Type
- conference paper
- Language
- en
- Open access
- diamond
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: Big data security and privacy in healthcare: A Review. https://app.cochat.ai/papers/10.1016/j.procs.2017.08.292
- The paper
- Abouelmehdi, K., Beni-Hssane, A., Khaloufi, H., & Saadi, M. (2017). Big data security and privacy in healthcare: A Review. Procedia Computer Science. https://doi.org/10.1016/j.procs.2017.08.292
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.