The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
L -diversity: Privacy beyond k -anonymity
Abstract
Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k -anonymity has gained popularity. In a k -anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes. In this article, we show using two simple attacks that a k -anonymized dataset has some subtle but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and we show that k -anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks, and we propose a novel and powerful privacy criterion called ℓ-diversity that can defend against such attacks. In addition to building a formal foundation for ℓ-diversity, we show in an experimental evaluation that ℓ-diversity is practical and can be implemented efficiently.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“To address these shortcomings, Machanavajjhala et al. [162] proposed l-diversity, which ensures that each sensitive attribute of the user should have l distinct values for every equivalence class, which share the same quasi-identifier values.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [162]
In the citing paper
“Key approaches (e.g.[194,205,276]) seek to protect speciic individuals and groups from being identiiable, i.e., minimize disclosure risk.”
From Fairness in machine learning: A survey (2024) · cites this paper as [205]
Citations
Cites
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,454
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
Goldreich, O., Micali, S., & Wigderson, A. (1987) · Proceedings of the nineteenth annual ACM conference on Theory of computing - STOC '87 · doi:10.1145/28395.28420 · Cited by 3,590
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480775 · Cited by 3,261
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 3,009
Completeness theorems for non-cryptographic fault-tolerant distributed computation
Ben-Or, M., & Wigderson, A. (1988) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/62212.62213 · Cited by 2,529
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 2,436
Self-citationProtecting respondents identities in microdata release
Samarati, P. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.971193 · Cited by 2,339
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335438 · Cited by 1,719
Research works printed but not linked: 4
- Cox , L. H. 1982 . Solving confidentiality protection problems in tabulations using network optimization: A network model for cell suppression in the u.s. economic censuses . In Proceedings of the International Seminar on Statistical Confidentiality. Dublin International Statistical Institute , Dublin, Ireland. 229--245. Cox, L. H. 1982. Solving confidentiality protection problems in tabulations using network optimization: A network model for cell suppression in the u.s. economic censuses. In Proceedings of the International Seminar on Statistical Confidentiality. Dublin International Statistical Institute, Dublin, Ireland. 229--245.
- Cox , L. H. 1987 . New results in dislosure avoidance for tabulations . In Proceedings of the International Statistical Institute 46th Session . Tokyo, Japan. 83--84. Cox, L. H. 1987. New results in dislosure avoidance for tabulations. In Proceedings of the International Statistical Institute 46th Session. Tokyo, Japan. 83--84.
- 10.5555/1316689.1316701 · DOI
- Sweeney , L. 2000. Uniqueness of simple demographics in the u.s. population. Tech. rep ., Carnegie Mellon University . Sweeney, L. 2000. Uniqueness of simple demographics in the u.s. population. Tech. rep., Carnegie Mellon University.
Cited by
Sorted by citations
Items 601–700 of 1,006
Provable De-anonymization of Large Datasets with Sparse Dimensions
Datta, A., Sharma, D., & Sinha, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28641-4_13 · Cited by 33
On Privacy-Preservation of Text and Sparse Binary Data with Sketches
Aggarwal, C. C., & Yu, P. S. (2007) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972771.6 · Cited by 33
Mastering data privacy: leveraging K-anonymity for robust health data sharing
Karagiannis, S., Ntantogian, C., Magkos, E., Tsohou, A., & Ribeiro, L. L. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00838-8 · Cited by 32
An event-based platform for collaborative threats detection and monitoring
Lodi, G., Aniello, L., Luna, G. A. D., & Baldoni, R. (2013) · Information Systems · doi:10.1016/j.is.2013.07.005 · Cited by 32
Zhang, X., Liu, C., Nepal, ., Yang, C., Dou, W., & Chen, J. (2013) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2013.235 · Cited by 32
The Importance of Context: Risk-based De-identification of Biomedical Data
Kuhn, K. A., Praßer, F., & Kohlmayer, F. (2016) · Methods of Information in Medicine · doi:10.3414/me16-01-0012 · Cited by 32
An improved anonymity model for big data security based on clustering algorithm
Yin, C., Zhang, S., Xi, J., & Wang, J. (2016) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3902 · Cited by 32
Towards Real-Time Road Traffic Analytics using Telco Big Data
Costa, C., Chatzimilioudis, G., Demetrios Zeinalipour-yazti, A., & Mokbel, M. F. (2017) · doi:10.1145/3129292.3129296 · Cited by 32
Guo, J., Yang, M., & Wan, B. (2021) · Symmetry · doi:10.3390/sym13061043 · Cited by 32
Privacy and anonymization for very large datasets
Muntés-Mulero, V., & Nin, J. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646333 · Cited by 32
An entropy based method for measuring anonymity
Michele Bezzi, A. (2007) · Security and Privacy in Communication Networks · doi:10.1109/seccom.2007.4550303 · Cited by 32
On the Unicity of Smartphone Applications
Achara, J. P., Ács, G., & Castelluccia, C. (2015) · ACM Workshop on Privacy in the Electronic Society (WPES) · doi:10.1145/2808138.2808146 · Cited by 32
Preserving Anonymity of Recurrent Location-Based Queries
Riboni, D., Pareschi, L., Bettini, C., & Jajodia, S. (2009) · International Symposium on Temporal Representation and Reasoning · doi:10.1109/time.2009.8 · Cited by 32
Preserving Privacy through Data Generation
Vreeken, J., van Leeuwen, M., & Siebes, A. P. J. M. (2007) · Proceedings · doi:10.1109/icdm.2007.25 · Cited by 32
Collaborative analytics for data silos
Kim, J., Ha, H., Chun, B.-G., Yoon, S., & Cha, S. K. (2016) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2016.7498286 · Cited by 32
Practical Differentially Private Top-$k$ Selection with Pay-what-you-get CompositionPreprint
Durfee, D., & Rogers, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04273 · Cited by 32
Improving Data Utility Through Game Theory in Personalized Differential Privacy
Cui, L., Qu, Y., Nosouhi, M. R., Yu, S., Niu, J.-W., & Xie, G. (2019) · Journal of Computer Science and Technology · doi:10.1007/s11390-019-1910-3 · Cited by 31
Naresh, V. S., & Reddi, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01098-6 · Cited by 31
Privacy-preserving data cube for electronic medical records: An experimental evaluation
Kim, S.-H., Lee, H., & Chung, Y. D. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.09.008 · Cited by 31
Identity vs. Attribute Disclosure Risks for Users with Multiple Social Profiles
Andreou, A., Goga, O., & Loiseau, P. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3110046 · Cited by 31
Incentive Mechanism Design for Crowdsourcing-Based Indoor Localization
Li, W., Zhang, C., Liu, Z., & Tanaka, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2869202 · Cited by 31
Domadiya, N. H., & Rao, U. P. (2020) · Computing · doi:10.1007/s00607-020-00847-0 · Cited by 31
Big Data Warehouse for Healthcare-Sensitive Data Applications
Shahid, A., Nguyen, T.-A. N., & Kechadi, T. (2021) · Sensors · doi:10.3390/s21072353 · Cited by 31
Context-Aware Security for Vehicles and Fleets: A Survey
Grimm, D., Stang, M., & Sax, E. (2021) · IEEE Access · doi:10.1109/access.2021.3097146 · Cited by 31
DeepFixCX : Explainable privacy‐preserving image compression for medical image analysis
Gaudio, A., Smailagic, A., Faloutsos, C., Mohan, S., Johnson, E., Liu, Y., Costa, P., & Campilho, A. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1495 · Cited by 34
A review of federated learning: taxonomy, privacy and future directions
Ratnayake, H., Chen, L., & Ding, X. (2023) · Journal of Intelligent Information Systems · doi:10.1007/s10844-023-00797-x · Cited by 32
A personalized two-tier cloaking scheme for privacy-aware location-based services
Niu, B., Zhu, X., Li, W., Li, H., Wang, Y., & Lu, Z. (2015) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2015.7069322 · Cited by 31
Abdelhameed, S. A., Moussa, S. M., & Khalifa, M. E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.08.017 · Cited by 31
Anonymizing Unstructured DataPreprint
Motwani, R., & Nabar, S. U. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0810.5582 · Cited by 31
Methods and tools for healthcare data anonymization: a literature review
Vovk, O., Piho, G., & Ross, P. (2023) · International Journal of General Systems · doi:10.1080/03081079.2023.2173749 · Cited by 30
Privacy Preserving Through Fireworks Algorithm Based Model for Image Perturbation in Big Data
Rahmani, A., Amine, A., Hamou, R. M., Rahmani, M. E., & Bouarara, H. A. (2015) · International Journal of Swarm Intelligence Research · doi:10.4018/ijsir.2015070103 · Cited by 30
Challenges in Secure Sensor-Cloud Computing
Poolsappasit, N., Kumar, V., Madria, S., & Chellappan, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23556-6_5 · Cited by 30
3PLUS: Privacy-preserving pseudo-location updating system in location-based services
Niu, B., Zhu, X., Chi, H., & Li, H. (2013) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2013.6555314 · Cited by 30
A general framework for privacy preserving data publishing
Sattar, A. H. M. S., Li, J., Ding, X., Liu, J., & Vincent, M. W. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.09.022 · Cited by 30
Analysis-preserving protection of user privacy against information leakage of social-network Likes
Buccafurri, F., Fotia, L., Lax, G., & Saraswat, V. (2015) · Information Sciences · doi:10.1016/j.ins.2015.08.046 · Cited by 30
Cryptography for Security and Privacy in Cloud Computing
Raß, S., & Slamanig, D. (2013) · Cited by 30
Lightning: Utility-Driven Anonymization of High-Dimensional Data
Praßer, F., Bild, R., Eicher, J., Spengler, H., Kohlmayer, F., & Kuhn, K. A. (2016) · Transactions on data privacy · Cited by 30
τ -safety: A privacy model for sequential publication with arbitrary updates
Anjum, A., Raschia, G., Gelgon, M., Khan, A., Malik, S. U. R., Ahmad, N., Ahmed, M., Suhail, S., & Alam, M. (2017) · Computers & Security · doi:10.1016/j.cose.2016.12.014 · Cited by 30
A Scalable and Pragmatic Method for the Safe Sharing of High-Quality Health Data
Praßer, F., Kohlmayer, F., Spengler, H., & Kuhn, K. A. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2676880 · Cited by 30
Yu, L., Motipalli, S. M., Lee, D., Liu, P., Xu, H., Liu, Q., Tan, J., & Luo, B. (2018) · ACM Symposium on Access Control Models and Technologies (SACMAT) · doi:10.1145/3205977.3205981 · Cited by 30
Anonymization Techniques for Protecting Privacy: A Survey
Pawar, A., Ahirrao, S. N., & Churi, P. P. (2018) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon.2018.8745425 · Cited by 30
#DontTweetThis: Scoring Private Information in Social Networks
Wang, Q., Xue, H., Li, F., Lee, D., & Luo, B. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0059 · Cited by 30
Privacy Technology to Support Data Sharing for Comparative Effectiveness Research
Jiang, X., Sarwate, A. D., & Lucila Ohno-machado, A. (2013) · Medical Care · doi:10.1097/mlr.0b013e31829b1d10 · Cited by 30
Privacy-Preserving Data Mining: A Survey
Aggarwal, C. C., & Yu, P. S. (2007) · Springer eBooks · doi:10.1007/978-0-387-48533-1_18 · Cited by 30
Hua, J., Tang, A., Fang, Y., Shen, Z., & Zhong, S. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2532839 · Cited by 30
Differentially Private Frequent Sequence Mining
Xu, S., Cheng, X., Su, S., Xiao, K., & Xiong, L. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2601106 · Cited by 30
Challenges on Anonymity, Privacy, and Big Data
Basso, T., Matsunaga, R. M., Moraes, R., & Antunes, N. (2016) · Latin-American Symposium on Dependable Computing · doi:10.1109/ladc.2016.34 · Cited by 30
($k$,$ε$)-Anonymity: $k$-Anonymity with $ε$-Differential PrivacyPreprint
Holohan, N., Antonatos, S., Braghin, S., & Aonghusa, P. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.01615 · Cited by 30
Evaluating the risk of patient re-identification from adverse drug event reports
Emam, K. E., Dankar, F. K., Neisa, A., & Jonker, E. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-114 · Cited by 31
A risk-based framework for biomedical data sharing
Dankar, F. K., & Badji, R. M. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.01.012 · Cited by 29
Federation in genomics pipelines: techniques and challenges
Chaterji, S., Koo, J., Li, N., Meyer, F., Grama, A. Y., & Bagchi, S. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx102 · Cited by 29
Efficient machine learning over encrypted data with non-interactive communication
Park, H., Kim, P., Kim, H., Park, K.-W., & Lee, Y. (2017) · Computer Standards & Interfaces · doi:10.1016/j.csi.2017.12.004 · Cited by 29
A Micro-Aggregation Algorithm Based on Density Partition Method for Anonymizing Biomedical Data
Wu, X., Wei, Y., Jiang, T., Wang, Y., & Jiang, S. (2019) · Current Bioinformatics · doi:10.2174/1574893614666190416152025 · Cited by 29
Sensitive Label Privacy Preservation with Anatomization for Data Publishing
Yao, L., Chen, Z., Wang, X., Liu, D., & Wu, G. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2919833 · Cited by 29
Privacy-preserving location data stream clustering on mobile edge computing and cloud
Stephanie, V., Chamikara, M. A. P., Khalil, I., & Atiquzzaman, M. (2021) · Information Systems · doi:10.1016/j.is.2021.101728 · Cited by 29
Privacy preserving data sharing and analysis for edge-based architectures
Sheikhalishahi, M., Saracino, A., Martinelli, F., & Marra, A. L. (2021) · International Journal of Information Security · doi:10.1007/s10207-021-00542-x · Cited by 29
Rao, J., Gao, S., Li, M., & Huang, Q. (2021) · Transactions in GIS · doi:10.1111/tgis.12769 · Cited by 29
Differential Privacy in Natural Language Processing The Story So Far
Klymenko, A., Meisenbacher, S., & Matthes, F. (2022) · Workshop on Privacy in Natural Language Processing (PrivateNLP) · doi:10.18653/v1/2022.privatenlp-1.1 · Cited by 29
A systematic overview on methods to protect sensitive data provided for various analyses
Templ, M., & Sariyar, M. (2022) · International Journal of Information Security · doi:10.1007/s10207-022-00607-5 · Cited by 31
Statistical Data Privacy: A Song of Privacy and Utility
Slavković, A., & Seeman, J. (2022) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-033121-112921 · Cited by 35
Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things
Sangaiah, A. K., Javadpour, A., Ja’fari, F., Pinto, P., & Chuang, H.-M. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3271591 · Cited by 29
Sharing Data and Models in Software Engineering
Menzies, T., Kocagüneli, E., Turhan, B., Minku, L. L., & Peters, F. (2014) · Cited by 29
De-identifying a public use microdata file from the Canadian national discharge abstract database
Emam, K. E., Paton, D., Dankar, F. K., & Koru, G. (2011) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-11-53 · Cited by 29
Privacy modeling for video data publication
Saini, M. K., Atrey, P. K., Mehrotra, S., Emmanuel, S., & Kankanhalli, M. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5583334 · Cited by 29
Efficient differentially private learning improves drug sensitivity prediction
Honkela, A., Das, M., Nieminen, A., Dikmen, O., & Samuel Kaski, A. (2018) · Biology Direct · doi:10.1186/s13062-017-0203-4 · Cited by 29
Privacy-Aware in the IoT Applications: A Systematic Literature Review
Loukil, F., Ghedira-Guegan, C., Aïcha-Nabila, B., Boukadi, K., & Maamar, Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69462-7_35 · Cited by 29
GAN-DP: Generative Adversarial Net Driven Differentially Privacy-Preserving Big Data Publishing
Qu, Y., Yu, S., Zhang, J., Bình, H. T. T., Gao, L., & Zhou, W. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761070 · Cited by 29
Privacy Protection for Recommendation System: A Survey
Huang, W., Liu, B., & Tang, H. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1325/1/012087 · Cited by 28
Privacy Preservation over Big Data in Cloud Systems
Zhang, X., Liu, C., Nepal, ., Yang, C., & Chen, J. (2013) · Springer eBooks · doi:10.1007/978-3-642-38586-5_8 · Cited by 28
Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof
Holohan, N., Leith, D. J., & Mason, O. (2015) · Information Sciences · doi:10.1016/j.ins.2015.01.021 · Cited by 28
k-Anonymization by Freeform Generalization
Doka, K., Xue, M., Tsoumakos, D., & Karras, P. (2015) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/2714576.2714590 · Cited by 28
Differential Privacy in Practice
Nguyen, H. H., Kim, J. K., & Kim, Y.-H. (2013) · Journal of Computing Science and Engineering · doi:10.5626/jcse.2013.7.3.177 · Cited by 28
A Review of Differential Privacy in Individual Data Release
Wang, J., Liu, S., & Li, Y. (2015) · International Journal of Distributed Sensor Networks · doi:10.1155/2015/259682 · Cited by 28
Disclose More and Risk Less: Privacy Preserving Online Social Network Data Sharing
Chen, J., He, J., Cai, L., & Pan, J. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2018.2861403 · Cited by 28
k-Anonymous Decision Tree Induction
Friedman, A., Schuster, A., & Wolff, R. (2006) · Lecture notes in computer science · doi:10.1007/11871637_18 · Cited by 28
Tkachenko, O., Weinert, C., Schneider, T., & Hamacher, K. (2018) · Proceedings of the 2018 on Asia Conference on Computer and Communications Security · doi:10.1145/3196494.3196541 · Cited by 27
The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks
Goethals, S., Sörensen, K., & Martens, D. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3608482 · Cited by 39
Landscape of machine learning evolution: privacy-preserving federated learning frameworks and tools
Nguyen, G., Díaz, J. S.-P., Calatrava, A., Berberi, L., Lytvyn, O., Kozlov, V. Y., Tran, V., Moltó, G., & García, Á. L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11036-2 · Cited by 32
Towards a Better Understanding of Evaluating Trustworthiness in AI Systems
Kemmerzell, N., Schreiner, A., Khalid, H., Schalk, M., & Bordoli, L. (2025) · ACM Computing Surveys · doi:10.1145/3721976 · Cited by 34
Attasena, V., Harbi, N., & Darmont, J. (2015) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.2015040102 · Cited by 27
A top-down approach for approximate data anonymisation
Li, J., Yang, J., Zhao, Y., & Liu, B. (2012) · Enterprise Information Systems · doi:10.1080/17517575.2012.688223 · Cited by 27
Privacy by Design: From Technologies to Architectures
Antignac, T., & Métayer, D. L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06749-0_1 · Cited by 27
Big data set privacy preserving through sensitive attribute-based grouping
Qu, Y., Yu, S., Gao, L., & Niu, J. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7997113 · Cited by 27
Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives
Yan, K., Shen, W., Jin, Q., & Lu, H. (2019) · IEEE Access · doi:10.1109/access.2019.2894344 · Cited by 27
Decomposition+: Improving ℓ-Diversity for Multiple Sensitive Attributes
Das, D., & Bhattacharyya, D. K. (2012) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-27308-7_44 · Cited by 27
A Review on Privacy-Preserving Data Mining
Li, X., Yan, Z., & Zhang, P. (2014) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2014.135 · Cited by 27
Incentive Compatible Privacy-Preserving Distributed Classification
Nix, R., & Kantarciouglu, M. (2011) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2011.52 · Cited by 27
Measuring Privacy and Utility in Privacy‐Preserving Visualization
Dasgupta, A., Chen, M., & Kosara, R. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12142 · Cited by 27
Exploring re-identification risks in public domains
Ramachandran, A., Singh, L., Porter, E., & Nagle, F. (2012) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2012.6297917 · Cited by 27
Versatile publishing for privacy preservation
Jin, X., Zhang, M., Zhang, N., & Das, G. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835851 · Cited by 27
Krishnan, S., Wang, J., Franklin, M. J. M., Goldberg, K., & Kraska, T. (2016) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2882903.2915248 · Cited by 27
Personalized extended (α,k)‐anonymity model for privacy‐preserving data publishing
Liu, X., Xie, Q., & Wang, L. (2016) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3886 · Cited by 27
Emura, K., & Hayashi, T. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2763145 · Cited by 27
A Tool for Optimizing De-identified Health Data for Use in Statistical Classification
Praßer, F., Eicher, J., Bild, R., Spengler, H., & Kuhn, K. A. (2017) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2017.105 · Cited by 27
CAMPS: Efficient and privacy-preserving medical primary diagnosis over outsourced cloud
Hua, J., Shi, G., Zhu, H., Wang, F., Liu, X., & Li, H. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.054 · Cited by 27
On the Complexity of t-Closeness Anonymization and Related Problems
Liang, H., & Yuan, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37487-6_26 · Cited by 27
Navigational path privacy protection
Lee, K. C. K., Lee, W.-C., Leong, H. V., & Zheng, B. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646041 · Cited by 27
The Pursuit of Patterns in Educational Data Mining as a Threat to Student Privacy
Kyritsi, K. H., Zorkadis, V., Stavropoulos, E. C., & Verykios, V. S. (2019) · Journal of Interactive Media in Education · doi:10.5334/jime.502 · Cited by 26
Secure Federated Evolutionary Optimization—A Survey
Liu, Q., Yan, Y., Jin, Y., Wang, X., Ligeti, P., Yu, G., & Yan, X. (2023) · Engineering · doi:10.1016/j.eng.2023.10.006 · Cited by 28
A measure of variance for hierarchical nominal attributes
Domingo‐Ferrer, J., & Solanas, A. (2008) · Information Sciences · doi:10.1016/j.ins.2008.08.003 · Cited by 26
Metadata
- Authors
- Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M.
- Affiliations
- Cornell University (US)
- Venue
- ACM Transactions on Knowledge Discovery from Data
- Published
- 2007-03
- Volume
- 1
- Issue
- 1
- Pages
- 3
- ISSN
- 1556-4681 (ISSN-L)
- 1556-472X
- OpenAlex
- W2134167315
- Cited by
- 3,617
- References
- 72
- Citations by year
- 2026: 96 · 2025: 180 · 2024: 152 · 2023: 179 · 2022: 179 · 2021: 237 · 2020: 199 · 2019: 183 · 2018: 245 · 2017: 248 · 2016: 232 · 2015: 246 · 2014: 264 · 2013: 219 · 2012: 189
- FWCI
- 155.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Knowledge Discovery from Data (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Computer Networks and Communications › Access Control and Trust
- Keywords
- l-diversity, k-anonymity, privacy preservation, data anonymization, background knowledge attack, privacy attacks
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
