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Privacy-preserving data publishing: A survey of recent developments
Open accessAbstract
The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published and on agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this survey, we will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish PPDP from other related problems, and propose future research directions.
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“In particular, sensitive data disclosure is a long-standing challenge in protecting anonymity when data is published and/or analysed [6,111,195].”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
In the citing paper
“Yet, for correlated or otherwise related variables (often referred to as proxies or quasiidentiiers), much of the literature assumes a priori knowledge of the set of quasi-identiiers [111], or seeks to discover them on a case-by-case basis (e.g.[150,217]) towards the idea of privacy preserving data mining [6].”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
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“There is a tension here between advancing the fairness state of the art, privacy [111,268], and policy.”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
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Cited by
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Schwee, J. H., Sangogboye, F. C., & Kjærgaard, M. B. (2019) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3302505.3310064 · Cited by 7
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MethodMahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri
Canbay, Y., Vural, Y., & Sağıroğlu, Ş. (2019) · Journal of Polytechnic · doi:10.2339/politeknik.535184 · Cited by 7
“Temel mahremiyet korumalı veri yayınlama modellerinin sınıflandırılması (Classification of privacy preserving data publishing models) [25-28]”
InfluentialHsiao, M.-H., Lin, W.-Y., Hsu, K.-Y., et al. (2019) · doi:10.1109/embc.2019.8857025 · Cited by 7
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Phan, T. N., Dang, T. K., & Küng, J. (2011) · IDIMT · Cited by 7
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Sowmyarani, C. N., Namya, L. G., Nidhi, G. K., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3403372 · Cited by 6
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Min, G., & Oh, J. (2025) · IEEE Access · doi:10.1109/access.2025.3542266 · Cited by 7
Classification of technological privacy techniques for LTE-based public safety networks
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(k, ε)-Anonymity: An anonymity model for thwarting similarity attack
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Chen, R., Xie, M., & Lakshmanan, L. V. S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05813-9_15 · Cited by 6
“In the context of privacy-preserving data publishing, where an anonymized relational database is published, a plethora of privacy models have been proposed [17].”
BackgroundMethodSelf-citationPrivacy protection in open information management platforms
Gkoulalas-Divanis, A., & Aonghusa, P. M. (2014) · IBM Journal of Research and Development · doi:10.1147/jrd.2013.2285853 · Cited by 6
Privacy-preserving data mining demonstrator
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“These surveys often also reflect a bisection of the privacy-preserving data mining topic, which can easily be drawn from the PPDM literature.”
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Xiong, P., & Zhu, T. (2012) · International Conference on Management of e-Commerce and e-Government · doi:10.1109/icmecg.2012.14 · Cited by 6
“With several decades, more research has been developed and several effectual anonymous methods are brought out, which can be categorized into three types: generalization and suppression, anatomization and permutation, and perturbation[4].”
BackgroundDeferentially Private Tagging Recommendation Based on Topic Model
Zhu, T., Li, G., Zhou, W., Xiong, P., & Cao, Y. Y. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06608-0_46 · Cited by 6
Knowing Your Population: Privacy-Sensitive Mining of Massive Data
Sanches, P., Svee, E.-O., Bylund, M., Hirsch, B., & Boman, M. (2013) · Network and Communication Technologies · doi:10.5539/nct.v2n1p34 · Cited by 11
“We discovered techniques that seek to minimize the identification risk after a personal identifier is removed within privacy-preserving data publishing which deals precisely with our problem (Fung et al., 2010).”
BackgroundMethodAnonymization of Statistical Data
di Vimercati, S. D. C., Foresti, S., Livraga, G., et al. (2011) · it - Information Technology · doi:10.1524/itit.2011.0620 · Cited by 6
“Starting from -diversity and t-closeness, other proposals have been also developed, addressing peculiar aspects of the privacy problem or counteracting specific attacks [8].”
BackgroundHiding Emerging Patterns with Local Recoding Generalization
Cheng, M. W. K., Choi, B. K. K., & Cheung, W. K. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13657-3_19 · Cited by 6
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Gkoulalas-Divanis, A., & Loukides, G. (2015) · Springer eBooks · doi:10.1007/978-3-319-23633-9_2 · Cited by 6
ARM-Based Privacy Preserving for Medical Data Publishing
Fengli, Z., & Yijing, B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27051-7_6 · Cited by 6
Towards security and privacy in dynamic web service composition
Kassmi, I. E., & Jarir, Z. (2015) · World Conference on Complex Systems · doi:10.1109/icocs.2015.7483260 · Cited by 6
“Privacy issues In [8] the authors compare and evaluate the different approach of Privacy-preserving data publishing (PPDP) which provides Anonymity, Closeness or Isolation based methods for publishing information while preserving data privacy.”
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A Brief Survey on Privacy Preserving Techniques in Data Mining
Vinoth Kumar, N. J., & Santhi, V. (2016) · IOSR Journal of Computer Engineering · doi:10.9790/0661-1804054751 · Cited by 6
Utility enhanced anonymization for incomplete microdata
Gong, Q., Yang, M., Chen, Z., et al. (2016) · IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2016.7565966 · Cited by 6
“To preserve privacy during data publishing, many approaches [2] have been proposed.”
BackgroundProviding anonymity using top down specialization on Big Data using hadoop framework
Nandini Prasaad, K. S., & Pratheek, T. R. (2015) · IEEE India Conference · doi:10.1109/indicon.2015.7443205 · Cited by 6
“The current practice in data publishing [3] relies mainly on policies as to what types of data can be published and on agreements on the use of published data.”
MethodPrivacy-preservation in the integration and querying of multidimensional data models
Kwakye, M. M., & Barker, K. E. (2016) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2016.7906971 · Cited by 6
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Li, Z., & Li, S. (2017) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2017.8359960 · Cited by 6
“In recent years, differential privacy algorithms have developed a lot, but for classification algorithms, there is few support of differential privacy model [3].”
BackgroundA multi‐dimensional index for privacy‐preserving queries in cloud computing
Xu, H., Ding, X., Jin, H., et al. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5458 · Cited by 6
Heterogeneous differential privacy for vertically partitioned databases
Xia, Y., Zhu, T., Ding, X., Jin, H., & Zou, D. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5607 · Cited by 6
“We use the trade-off metric proposed by Fung et al10 to quantify our approach and optimize the method of privacy-preserving.”
MethodDifferential Privacy-Preserving User Linkage across Online Social Networks
Yao, X., Zhang, R., & Zhang, Y. (2021) · International Workshop on Quality of Service · doi:10.1109/iwqos52092.2021.9521333 · Cited by 6
“However, it cannot prevent probabilistic attack [40] in which an attacker infers sensitive information without recovering the user’s identity.”
BackgroundUtility-preserving differentially private skyline query
Lan, Q., Ma, J., Yan, Z., et al. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115871 · Cited by 6
Distance-based Techniques for Personal Microbiome Identification✱
Hittmeir, M., Mayer, R. L., & Ekelhart, A. (2022) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3538969.3538985 · Cited by 6
“The task of PMI is similar to record linkage ([6]), as we try to identify records in datasets that belong to the same individual.”
BackgroundGenerating synthetic mixed discrete-continuous health records with mixed sum-product networks
Kroes, S. K. S., van Leeuwen, M., Groenwold, R. H. H., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac184 · Cited by 6
Predicting The Disclosure of Personal Information on Social Networks: An Empirical Investigation
Buckel, T., & Thiesse, F. (2013) · Journal of the Association for Information Systems · Cited by 6
Pattern-Guided Data Anonymization and Clustering
Bredereck, R., Nichterlein, A., Niedermeier, R., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22993-0_19 · Cited by 6
“The notion of k-anonymity is a basic concept in privacy-preserving data publishing [9].”
BackgroundA Comparison of anonymization protection principles
Pinto, A. M. (2012) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2012.6303012 · Cited by 6
“We denote by E(I) the equivalence class that in a given relation contains the record for identity I and by E(r) the equivalence class that contains record r.”
MethodPrivacy-Preserving Release of Spatio-Temporal Density
Ács, G., Biczók, G., & Castelluccia, C. (2018) · Springer eBooks · doi:10.1007/978-3-319-98161-1_12 · Cited by 6
“We refer the interested reader to [21] and [56] for more details on privacy models and their usage.”
BackgroundSecurity of Data Science and Data Science for Security
Tellenbach, B., Rennhard, M., & Schweizer, R. (2019) · Springer eBooks · doi:10.1007/978-3-030-11821-1_15 · Cited by 6
Data Anonymization Based on Natural Equivalent Class
Guo, N., Yang, M., Gong, Q., et al. (2019) · IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2019.8791905 · Cited by 6
“Since these data include sensitive information, publishers are usually appointed to process the data by adopting the anonymization techniques to protect the sensitive information, and guarantee data utility [1].”
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Mao, J., & Liu, W. (2019) · IberLEF@SEPLN · Cited by 6
Risk Factors Identification for Heart Disease in Unstructured Dataset using Deep Learning Approach
Rajput, K., Chetty, G., & Davey, R. (2019) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2019.00154 · Cited by 6
Cooperative Privacy-Preserving Data Collection Protocol Based on Delocalized-Record Chains
Rodríguez-García, M., Cifredo-Chacón, M.-Á., & Quirós‐Olozábal, Á. (2020) · IEEE Access · doi:10.1109/access.2020.3028063 · Cited by 6
Liu, Z., Qamar, N., & Qian, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-642-53956-5_18 · Cited by 6
Protecting privacy in a clinical data warehouse
Kong, G., & Xiao, Z. (2014) · Health Informatics Journal · doi:10.1177/1460458213504204 · Cited by 6
Cost Effective Dynamic Concept Hierarchy Generation for Preserving Privacy
Vatsavayi, V. K., & Adusumalli, S. K. (2014) · Journal of Information & Knowledge Management · doi:10.1142/s021964921450035x · Cited by 6
How to Enhance Privacy Within DaaS Service Composition?
Tbahriti, S.-E., Ghedira-Guegan, C., Medjahed, B., et al. (2013) · IEEE Systems Journal · doi:10.1109/jsyst.2013.2244971 · Cited by 6
“2244971 tant efforts aimed at preserving privacy [32], privacy leakage”
BackgroundPublishing Differentially Private Datasets via Stable Microaggregation
Iftikhar, M., Wang, Q., & Lin, Y. (2019) · Movebank · doi:10.5441/002/edbt.2019.81 · Cited by 6
Uncertain Time-Series Similarity: Return to the BasicsPreprint
Dallachiesa, M., Nushi, B., Мирыленка, К., et al. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1208.1931 · Cited by 6
“In these applications privacy is a major concern, addressed by various privacy-preserving transforms [2, 11, 20], which introduce data uncertainty.”
BackgroundA Computer Science Academic Vocabulary List
Roesler, D. (2000) · doi:10.15760/etd.7414 · Cited by 8
Privacy Preserving Data FusionPreprint
Tian, L., Turjeman, D., & Levy, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4451656 · Cited by 6
Xu, H., & Dinev, T. (2022) · MIS Quarterly · doi:10.25300/misq/2022/464e2 · Cited by 6
Privacy Disclosure and Preservation in Learning with Multi-Relational Databases
Guo, H., Viktor, H. L., & Paquet, E. (2011) · Journal of Computing Science and Engineering · doi:10.5626/jcse.2011.5.3.183 · Cited by 5
“Often, data anonymization operations are applied [9].”
BackgroundPowar, J., Janssen, H., Cloete, R., & Singh, J. P. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732164 · Cited by 5
Husseini, F. E., Noura, H., & Vernier, F. (2025) · Annals of Telecommunications · doi:10.1007/s12243-025-01107-y · Cited by 5
“PPDP ensures the confidentiality of individuals’ data during research and analysis by implementing adjustments to avoid their identification [31], [32].”
Cloud-assisted distributed private data sharing
Chen, F., Mohammed, N., Wang, S., He, W., Cheng, S. S. M., & Jiang, X. (2015) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2808719.2808740 · Cited by 5
“Their methods [8,16] adopt k-anonymity [22] and its extensions [4] as the underlying privacy principle.”
MethodPrivacy Preserving Over Big Data Through VSSFA and MapReduce Framework in Cloud Environment
Thiyagarajan, V. S., & Ayyasamy, A. (2017) · Wireless Personal Communications · doi:10.1007/s11277-017-4836-5 · Cited by 5
“Data anonymization is an innovative hopeful type of method employed to accomplish this objective [17].”
Methodk-Anonymity on Graphs Using the Szemerédi Regularity Lemma
Minello, G., Rossi, L., & Andrea Torsello, A. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3020329 · Cited by 5
Analysis and Protection of Public Medical Dataset: From Privacy Perspective
Jahan, S., Ge, Y.-F., Kabir, E., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7108-4_7 · Cited by 5
Mayer, R. L., Hittmeir, M., & Ekelhart, A. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103538 · Cited by 5
Identifying key factors for the development of the Industrial Metaverse
Müller, M., Bohné, T., & Jamboula, T. (2023) · 2023 IEEE Smart World Congress (SWC) · doi:10.1109/swc57546.2023.10448572 · Cited by 3
“For data sharing to be scaled effectively, companies, governments, and other organisations will need to approach data publishing with privacy as a priority to protect workers and company information [23].”
BackgroundInfluentialAnonymizing Social Network Data for Maximal Frequent-Sharing Pattern Mining
Fung, B. C. M., Jin, Y., Li, J., et al. (2015) · Lecture notes in social networks · doi:10.1007/978-3-319-14379-8_5 · Cited by 5
Self-citationAn improved Ambiguity+ anonymization technique with enhanced data utility
Rajaei, M., & Haghjoo, M. S. (2015) · Conference on Information and Knowledge Technology · doi:10.1109/ikt.2015.7288743 · Cited by 5
“This problem is known as Privacy-preserving data publishing (PPDP) [1].”
BackgroundPrivacy Preserving for Tagging Recommender Systems
Zhu, T., Li, G., Ren, Y., et al. (2013) · IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT) · doi:10.1109/wi-iat.2013.12 · Cited by 5
“An adversary with background information may re-identify a particular user in a tagging dataset and obtain the user’s historical tagging records [2].”
BackgroundRespecting Privacy in Web Service Composition
Tbahriti, S.-E., Medjahed, B., Ghedira-Guegan, C., et al. (2013) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2013.28 · Cited by 5
“Indeed, despite important efforts aimed at preserving privacy [2], privacy leakage incidents on the Web continue to make the headlines.”
BackgroundEvaluating the Impact of Anonymization on Large Interaction Network Datasets
Silva, M. J., Rijo, P., & Francisco, A. P. (2014) · doi:10.1145/2663715.2669610 · Cited by 5
Randomized Addition of Sensitive Attributes for l-diversity
Sei, Y., & Ohsuga, A. (2014) · International Conference on Security and Cryptography · doi:10.5220/0005058203500360 · Cited by 5
“…that explicit identify individuals (e.g., name and phone number), QIDs are attributes that could be potentially combined with public directories to identify individuals (e.g., zip code, age), and sensitive attributes are personal attributes of private nature (e.g., disease) (Fung et al., 2010).”
BackgroundRandom Response Forest for Privacy-Preserving Classification
Szücs, G. (2013) · Journal of Computational Engineering · doi:10.1155/2013/397096 · Cited by 5
A Review of Privacy Preservation Technique
KumarSingh, A., Keer, N. P., & Motwani, A. (2014) · International Journal of Computer Applications · doi:10.5120/15554-4239 · Cited by 5
“Also explain in paper [4] t-closeness is not suitable method for privacy preservation of publish data.”
BackgroundMethodQuantifying fine-grained privacy risk and representativeness in medical data
Jiang, X., Cheng, S. S. M., & Lucila Ohno-machado, A. (2011) · doi:10.1145/2023582.2023594 · Cited by 5
Metadata
- Authors
- Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S.
- Affiliations
- Concordia University (CA)
- Simon Fraser University (CA)
- University of Illinois Chicago (US)
- Venue
- ACM Computing Surveys
- Published
- 2010-06
- Volume
- 42
- Issue
- 4
- Pages
- 1-53
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2142406320
- Semantic Scholar
- 1fbf7bed25864b57b47daf212523bd036dc7ab5e
- Cited by
- 1,704
- References
- 156
- Citations by year
- 2026: 31 · 2025: 59 · 2024: 51 · 2023: 66 · 2022: 84 · 2021: 106 · 2020: 105 · 2019: 113 · 2018: 128 · 2017: 129 · 2016: 155 · 2015: 145 · 2014: 172 · 2013: 123 · 2012: 98
- FWCI
- 116.13
- Type
- journal article
- Language
- en
- Open access
- green
- summit.sfu.ca
- Locations
- ACM Computing Surveys (publishedVersion)
- Summit (Simon Fraser University) (submittedVersion)
- eScholarship@McGill (McGill) (submittedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada (356065-2008)
- 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 › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving data publishing, data privacy, sensitive data, data publication, data distortion, data exchange, data protection, privacy-preserving data mining
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