Enhancing data utility in differential privacy via microaggregation-based $$k$$ k -anonymity
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The state-of-the-art schemes in medical data areas rely more or less on the adoption of certain cryptographic primitives to implement authentication, access control, key management, and privacy protection for medical information.
“The state-of-the-art schemes [30], [47]- [52], [55], [57] in medical data areas rely more or less on the adoption of certain cryptographic primitives to implement authentication, access control, key management, and privacy protection for medical information.”
From A review of secure and privacy-preserving medical data sharing (2019) · cites this paper as [55]
Cites
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190 · see citing works
Kilgarriff, A. (2000) · Language · doi:10.2307/417141 · Cited by 11,595 · see citing works
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 · see citing works
Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11787006_1 · Cited by 5,945 · see citing works
(1998) · The MIT Press eBooks · doi:10.7551/mitpress/7287.001.0001 · Cited by 4,849 · see citing works
<i>L</i> -diversity: Privacy beyond <i>k</i> -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617 · see citing works
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 · see citing works
Verbs semantics and lexical selection
Wu, Z., & Palmer, M. S. (1994) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/981732.981751 · Cited by 3,157 · see citing works
L-diversity: privacy beyond k-anonymity
Machanavajjhala, A., Gehrke, J. E., Kifer, D., & Venkitasubramaniam, M. (2006) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2006.1 · Cited by 2,397 · see citing works
Protecting respondents identities in microdata release
Samarati, P. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.971193 · Cited by 2,339 · see citing works
Development and application of a metric on semantic nets
Rada, R., Mili, H., Bicknell, E. J., & Blettner, M. (1989) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.24528 · Cited by 1,835 · see citing works
Samarati, P., & Sweeney, L. (2018) · doi:10.1184/r1/6625469 · Cited by 1,341 · see citing works
Towards Sharp Inapproximability For Any 2-CSP
Austrin, P. (2007) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2007.41 · Cited by 1,339 · see citing works
McSherry, F. (2009) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1559845.1559850 · Cited by 1,146 · see citing works
An approach for measuring semantic similarity between words using multiple information sources
Li, Y., Bandar, Z. A., & McLean, D. (2003) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2003.1209005 · Cited by 1,090 · see citing works
Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, B., Raskhodnikova, S., & Smith, A. (2007) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/1250790.1250803 · Cited by 994 · see citing works
A firm foundation for private data analysis
Dwork, C. (2010) · Communications of the ACM · doi:10.1145/1866739.1866758 · Cited by 858 · see citing works
Wong, R. C.-W., Li, J., Fu, A. W.-C., & Wang, K. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150499 · Cited by 626 · see citing works
Practical data-oriented microaggregation for statistical disclosure control
Domingo-Ferrer, J., & Mateo‐Sanz, J. M. (2002) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.979982 · Cited by 579 · see citing works
Self-citationTop-Down Specialization for Information and Privacy Preservation
Fung, B. C. M., Wang, K., & Yu, P. S. (2005) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2005.143 · Cited by 578 · see citing works
Boosting the accuracy of differentially private histograms through consistency
Hay, M., Rastogi, V., Miklau, G., & Suciu, D. M. (2010) · Proceedings of the VLDB Endowment · doi:10.14778/1920841.1920970 · Cited by 530 · see citing works
A learning theory approach to non-interactive database privacy
Blum, A. L., Ligett, K., & Roth, A. (2008) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/1374376.1374464 · Cited by 514 · see citing works
Privacy: Theory meets Practice on the Map
Machanavajjhala, A., Kifer, D., Abowd, J. M., Gehrke, J. E., & Vilhuber, L. (2008) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2008.4497436 · Cited by 468 · see citing works
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Domingo‐Ferrer, J., & Torra, V. (2005) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-005-0007-5 · Cited by 434 · see citing works
Self-citationDifferentially Private Spatial Decompositions
Cormode, G., Procopiuc, C. M., Srivastava, D., Shen, E., & Yu, T. (2012) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2012.16 · Cited by 412 · see citing works
Statistical Disclosure Control
Hundepool, A., Domingo‐Ferrer, J., Franconi, L., Giessing, S., Nordholt, E. S., Spicer, K., & de Wolf, P. (2012) · Wiley eBooks · doi:10.1002/9781118348239 · Cited by 391 · see citing works
Self-citationDifferential Privacy via Wavelet Transforms
Xiao, X., Wang, G., & Gehrke, J. E. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.247 · Cited by 373 · see citing works
On the complexity of differentially private data release
Dwork, C., Naor, M., Reingold, O., Rothblum, G. N., & Vadhan, S. (2009) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/1536414.1536467 · Cited by 363 · see citing works
Differentially private data release for data mining
Mohammed, N., Chen, R., Fung, B. C. M., & Yu, P. S. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020487 · Cited by 328 · see citing works
Statistical Disclosure Control in Practice
Willenborg, L., & de Waal, T. (1996) · Lecture notes in statistics · doi:10.1007/978-1-4612-4028-0 · Cited by 302 · see citing works
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., & Zhu, A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30570-5_17 · Cited by 302 · see citing works
A simple and practical algorithm for differentially private data releasePreprint
Hardt, M., Ligett, K., & McSherry, F. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1012.4763 · Cited by 296 · see citing works
Differentially private grids for geospatial data
Qardaji, W., Yang, W., & Li, N. (2013) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2013.6544872 · Cited by 265 · see citing works
Ontology-based information content computation
Sánchez, D., Batet, M., & Isern, D. (2010) · Knowledge-Based Systems · doi:10.1016/j.knosys.2010.10.001 · Cited by 258 · see citing works
Self-citationA semantic similarity metric combining features and intrinsic information content
Pirrò, G. (2009) · Data & Knowledge Engineering · doi:10.1016/j.datak.2009.06.008 · Cited by 241 · see citing works
Minimum spanning tree partitioning algorithm for microaggregation
Laszlo, M. J., & Mukherjee, S. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.112 · Cited by 223 · see citing works
Publishing set-valued data via differential privacy
Chen, R., Mohammed, N., Fung, B. C. M., et al. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/3402707.3402744 · Cited by 221 · see citing works
Mechanism Design via Differential Privacy
McSherry, F., & Talwar, K. (2007) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2007.4389483 · Cited by 211 · see citing works
Statistical Disclosure Control
Franconi, L., & Hundepool, A. (2012) · Medical Entomology and Zoology · Cited by 205 · see citing works
On sampling, anonymization, and differential privacy or, k -anonymization meets differential privacy
Li, N., Qardaji, W., & Su, D. (2012) · ACM Symposium on Information, Computer and Communications Security · doi:10.1145/2414456.2414474 · Cited by 205 · see citing works
Differentially Private Data Release through Multidimensional Partitioning
Xiao, Y., Xiong, L., & Yuan, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15546-8_11 · Cited by 167 · see citing works
A Critique of k-Anonymity and Some of Its Enhancements
Domingo‐Ferrer, J., & Torra, V. (2008) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2008.97 · Cited by 158 · see citing works
Self-citationSánchez, D., & Batet, M. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.03.013 · Cited by 155 · see citing works
Self-citationEfficient multivariate data-oriented microaggregation
Domingo‐Ferrer, J., Martínez-Ballesté, A., Mateo‐Sanz, J. M., & Sebé, F. (2006) · The VLDB Journal · doi:10.1007/s00778-006-0007-0 · Cited by 149 · see citing works
Self-citationDisclosure Risk Assessment in Perturbative Microdata Protection
Yancey, W. E., Winkler, W. E., & Creecy, R. H. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47804-3_11 · Cited by 146 · see citing works
X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies
Petrakis, E. G. M., Varelas, G., Hliaoutakis, A., et al. (2006) · Cited by 139 · see citing works
Differentially Private Histogram Publication
Xu, J. W., Zhang, Z., Xiao, X., Yang, Y., & Yu, G. (2012) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2012.48 · Cited by 136 · see citing works
A polynomial-time approximation to optimal multivariate microaggregation
Domingo‐Ferrer, J., Sebé, F., & Solanas, A. (2007) · Computers & Mathematics with Applications · doi:10.1016/j.camwa.2007.04.034 · Cited by 130 · see citing works
Self-citationGrover, C., Brew, C., Manandhar, S., et al. (1994) · Cited by 90 · see citing works
Hybrid microdata using microaggregation
Domingo-Ferrer, J., & González-Nicolás, Ú. (2010) · Information Sciences · doi:10.1016/j.ins.2010.04.005 · Cited by 65 · see citing works
Self-citationAnonymization of nominal data based on semantic marginality
Domingo‐Ferrer, J., Sánchez, D., & Rufian-Torrell, G. (2013) · Information Sciences · doi:10.1016/j.ins.2013.04.021 · Cited by 60 · see citing works
Self-citationDensity-based microaggregation for statistical disclosure control
Lin, J.-L., Wen, T.-H., Hsieh, J.-C., & Chang, P. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.09.054 · Cited by 59 · see citing works
Semantic adaptive microaggregation of categorical microdata
Mart́ınez, S., Sánchez, D., & Valls, A. (2012) · Computers & Security · doi:10.1016/j.cose.2012.04.003 · Cited by 55 · see citing works
Self-citationOn Syntactic Anonymity and Differential Privacy
Clifton, C., & Tassa, T. (2013) · Transactions on data privacy · doi:10.5555/2612167.2612170 · Cited by 51 · see citing works
A New Model to Compute the Information Content of Concepts from Taxonomic Knowledge
Sánchez, D., & Batet, M. (2012) · International Journal on Semantic Web and Information Systems · doi:10.4018/jswis.2012040102 · Cited by 50 · see citing works
Self-citationLHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection
Dandekar, R. A., Domingo‐Ferrer, J., & Sebé, F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47804-3_12 · Cited by 47 · see citing works
Self-citationEmpirical privacy and empirical utility of anonymized data
Cormode, G., Procopiuc, C. M., Shen, E., Srivastava, D., & Yu, T. (2013) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icdew.2013.6547431 · Cited by 42 · see citing works
Improving the Utility of Differentially Private Data Releases via k-Anonymity
Soria-Comas, J., Domingo‐Ferrer, J., Sánchez, D., et al. (2013) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2013.47 · Cited by 38 · see citing works
Self-citationSemantically-grounded construction of centroids for datasets with textual attributes
́nez, S. M., Valls, A., & Sánchez, D. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.04.030 · Cited by 37 · see citing works
Self-citationEfficient Anonymizations with Enhanced Utility
Goldberger, J., & Tassa, T. (2010) · Cited by 36 · see citing works
Empirical Evaluation of Statistical Inference from Differentially-Private Contingency Tables
Charest, A.-S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33627-0_20 · Cited by 12 · see citing works
Marginality: A Numerical Mapping for Enhanced Exploitation of Taxonomic Attributes
Domingo‐Ferrer, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34620-0_33 · Cited by 9 · see citing works
Self-citationA distance function to assess the similarity of words using ontologies
Sanromà, M. B., Mateu, A. V., & Oliveras, K. G. (2010) · Dialnet (Universidad de la Rioja) · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A review of secure and privacy-preserving medical data sharing
Jin, H., Luo, Y., Li, P., & Mathew, J. (2019) · IEEE Access · doi:10.1109/access.2019.2916503 · Cited by 293 · see citing works
A Review of Anonymization for Healthcare Data
Olatunji, I. E., Rauch, J., Katzensteiner, M., & Khosla, M. (2024) · Big Data · doi:10.1089/big.2021.0169 · Cited by 101 · see citing works
Machine learning and genomics: precision medicine versus patient privacy
Azencott, C.-A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2017.0350 · Cited by 89 · see citing works
- Authors
- Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., & Martínez, S.
- Affiliations
- Universitat Rovira i Virgili (ES)
- Corresponding author
- Sánchez, David
- Venue
- The VLDB Journal
- Published
- 2014-02-13
- Volume
- 23
- Issue
- 5
- Pages
- 771-794
- ISSN
- 0949-877X (ISSN-L)
- 1066-8888
- OpenAlex
- W1992286709
- Cited by
- 149
- References
- 64
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 3 · 2023: 11 · 2022: 13 · 2021: 7 · 2020: 18 · 2019: 13 · 2018: 24 · 2017: 22 · 2016: 8 · 2015: 12 · 2014: 6
- FWCI
- 14.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The VLDB Journal (publishedVersion)
- Repositori institucional URV (Universitat Rovira i Virgili) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- differential privacy, k-anonymity, microaggregation, data anonymization, data utility, noise reduction, privacy-preserving data publishing, privacy-utility trade-off
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