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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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Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop
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Jaillant, L., & Caputo, A. (2022) · AI & Society · doi:10.1007/s00146-021-01367-x · Cited by 94
“Although extensive research has been conducted in the area of privacy-preserving data publishing, much of this work was directed towards relational and statistical data, textual data remains a relatively unexplored area (Fung et al. 2010).”
BackgroundWhat are the trend and core knowledge of information security? A citation and co-citation analysis
Shiau, W., Wang, X., & Zheng, F. (2023) · Information & Management · doi:10.1016/j.im.2023.103774 · Cited by 74
An Axiomatic View of Statistical Privacy and Utility
Kifer, D., & Lin, B.-R. (2012) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v4i1.610 · Cited by 59
“For surveys on statistical privacy, see [10, 22, 1].”
BackgroundSarwate, A. D., Plis, S., Turner, J. A., Arbabshirani, M. R., & Calhoun, V. D. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00035 · Cited by 63
“Such approaches are used in official statistics and other settings—a survey of different privacy models can be found in Fung et al. (2010), and a survey of privacy technologies in a medical informatics context in Jiang et al. (2013).”
BackgroundPrivacy-respecting digital investigation
Dehghantanha, A., & Franke, K. (2014) · International Conference on Privacy, Security and Trust (PST) · doi:10.1109/pst.2014.6890932 · Cited by 59
“While this technique is looking sound in testing scale its adaptability to real situations is really questionable! Ignoring the fact that development of two different crypto schemes would cause a big system overhead, the author's assumption that investigators are only investigating by keyword search is far from realit…”
BackgroundCOAT: COnstraint-based anonymization of transactions
Loukides, G., Gkoulalas-Divanis, A., & Malin, B. (2010) · Knowledge and Information Systems · doi:10.1007/s10115-010-0354-4 · Cited by 60
“Moreover, the approach of [28] can harm data utility in mining applications, as a result of allowing instances of the same item in different transactions to be generalized differently [ 19 ].”
BackgroundEnhanced Secured Map Reduce layer for Big Data privacy and security
Jain, P., Gyanchandani, M., & Khare, N. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0193-4 · Cited by 61
Differentially private data sharing in a cloud federation with blockchain
Yang, M., Margheri, A., Hu, R., & Sassone, V. (2018) · IEEE Cloud Computing · doi:10.1109/mcc.2018.064181122 · Cited by 58
SynTEG: a framework for temporal structured electronic health data simulation
Zhang, Z., Yan, C., Lasko, T. A., Sun, J., & Malin, B. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa262 · Cited by 67
Differential private random forest
Patil, A., & Singh, S. (2014) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2014.6968348 · Cited by 58
“Many algorithms have been proposed to preserve privacy, but only few have considered classification algorithm in the differential privacy framework [8].”
BackgroundDisassociation for electronic health record privacy
Loukides, G., Liagouris, J., Gkoulalas-Divanis, A., & Terrovitis, M. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.05.009 · Cited by 59
“This scenario has many benefits [23] and allows wider and more effective use of the shared data.”
BackgroundAnalyzing multicore dumps to facilitate concurrency bug reproduction
Weeratunge, D., Zhang, X., & Jagannathan, S. (2010) · doi:10.1145/1736020.1736039 · Cited by 58
“Furthermore, recent techniques on anonymizing end-user information [10] to protect privacy apply naturally in our setting.”
BackgroundImproved l-diversity: scalable anonymization approach for privacy preserving big data publishing
Mehta, B. B., & Rao, U. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2019.08.006 · Cited by 57
“…considered to be one of the most popular privacy preserving data publishing approaches and suitable for big data analytics (Mehta et al., 2016). k -anonymity suffers with the record linkage attack (Fung et al., 2010) when there is an insufficient diversity between sensitive values in the dataset.”
QuerioCity: A Linked Data Platform for Urban Information Management
López, V., Kotoulas, S., Sbodio, M. L., Stephenson, M., Gkoulalas-Divanis, A., & Aonghusa, P. M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35173-0_10 · Cited by 57
“In addition, given the potentially sensitive nature of the information, the system provides functionality for detecting privacy threats and tools for anonymization [16].”
BackgroundProbabilistic k-anonymity through microaggregation and data swapping
Soria-Comas, J., & Domingo‐Ferrer, J. (2012) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2012.6251280 · Cited by 56
“Some good surveys on microdata anonymization are [1], [2], [3].”
BackgroundGame Theory Based Correlated Privacy Preserving Analysis in Big Data
Wu, X., Wu, T., Khan, M., Ni, Q., & Dou, W. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2701817 · Cited by 52
“As one of extensive applications in the big data processing, privacy-preserving data publication (PPDP) has attracted a lot of attention from academia and industry [4], [5].”
BackgroundPrivacy-Preserving Social Media Data Outsourcing
Zhang, J., Sun, J., Zhang, R., Zhang, Y., & Hu, X. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8486242 · Cited by 56
“Privacy-preserving data outsourcing has been thoroughly studied and surveyed in [29].”
BackgroundPrivacy-preserving heterogeneous health data sharing
Mohammed, N., Jiang, X., Chen, R., Fung, B. C. M., & Lucila Ohno-machado, A. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001027 · Cited by 54
Self-citationEnsuring location diversity in privacy-preserving spatio-temporal data publishing
Çiçek, A. E., Nergiz, M. E., & Saygın, Y. (2013) · The VLDB Journal · doi:10.1007/s00778-013-0342-x · Cited by 55
“[18] are surveys that shed light on the advances in this field.”
\(m\) -Privacy for Collaborative Data Publishing
Goryczka, S., Xiong, L., & Fung, B. C. M. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.18 · Cited by 55
Self-citationPreserving mobile subscriber privacy in open datasets of spatiotemporal trajectories
Gramaglia, M., Fiore, M., Tarable, A., & Banchs, A. (2017) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2017.8056979 · Cited by 58
“PPDP is defined as the development of methods for the publication of information that allows meaningful knowledge discovery, and yet preserves the privacy of monitored subjects [14].”
BackgroundMethodInfluentialA theory of pricing private data
Li, C., Li, D. Y., Miklau, G., & Suciu, D. M. (2017) · Communications of the ACM · doi:10.1145/3139457 · Cited by 59
Li, H., Xiong, L., Zhang, L., & Jiang, X. (2014) · Proceedings of the VLDB Endowment · doi:10.14778/2733004.2733059 · Cited by 59
“Privacy preserving data analysis and publishing [2] has received considerable attention in recent years as a promising approach for sharing information while preserving data privacy.”
BackgroundDynamic Privacy Pricing: A Multi-Armed Bandit Approach With Time-Variant Rewards
Xu, L., Jiang, C., Qian, Y., Zhao, Y., Li, J., & Ren, Y. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2611487 · Cited by 55
“Various solutions have been proposed to realize privacy-preserving data publishing (PPDP) [3] or privacy-preserving data mining (PPDM) [4].”
BackgroundWalsh, C. G., Xia, W., Li, M., Denny, J. C., Harris, P. A., & Malin, B. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917749652 · Cited by 55
Health data security and privacy: Challenges and solutions for the future
Yigzaw, K. Y., Olabarriaga, S. D., Michalas, A., Marco-Ruiz, L., Hillen, C., Verginadis, Y., de Oliveira, M. T., Krefting, D., Penzel, T., Bowden, J., et al. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823413-6.00014-8 · Cited by 62
Privacy Preserving RBF Kernel Support Vector Machine
Li, H., Xiong, L., Lucila Ohno-machado, A., & Jiang, X. (2014) · BioMed Research International · doi:10.1155/2014/827371 · Cited by 54
Modeling and evaluating information leakage caused by inferences in supply chains
Zhang, D. Y., Zeng, Y., Wang, L., et al. (2010) · Computers in Industry · doi:10.1016/j.compind.2010.10.002 · Cited by 53
LPL, Towards a GDPR-Compliant Privacy Language: Formal Definition and Usage
Gerl, A., Bennani, N., Harald Kosch, A., & Brunie, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-662-57932-9_2 · Cited by 53
Learning Anonymized Representations with Adversarial Neural Networks
Feutry, C., Piantanida, P., Bengio, Y., & Duhamel, P. (2018) · arXiv · doi:10.48550/arxiv.1802.09386 · Cited by 52
El–Sappagh, S., Alonso, J. M., Abuhmed, T., Ali, F., & Bugarín, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10415-5 · Cited by 58
A theory of pricing private data
Li, C., Li, D. Y., Miklau, G., & Suciu, D. M. (2013) · International Conference on Database Theory · doi:10.1145/2448496.2448502 · Cited by 61
Service-Oriented Architecture for High-Dimensional Private Data Mashup
Fung, B. C. M., Trojer, T., Hung, P. C. K., Xiong, L., Al-Hussaeni, K., & Rachida Dssouli, A. (2011) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2011.13 · Cited by 54
“Suppose that the adversary knows the target member is aMover and his age is 34.”
BackgroundSelf-citationShen, Y., & Jin, H. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978316 · Cited by 52
“A majority of existing methods [5, 6, 9, 10, 11, 19] are developed based on the scenarios that recommender servers are trusted such as the Netflix movie recommendation system.”
MethodDesign and evaluation of a data anonymization pipeline to promote Open Science on COVID-19
Jakob, C., Kohlmayer, F., Meurers, T., Vehreschild, J. J., & Praßer, F. (2020) · Scientific Data · doi:10.1038/s41597-020-00773-y · Cited by 52
TWIN-GPT : Digital Twins for Clinical Trials via Large Language Model
Wang, Y., Fu, T., Xu, Y., Ma, Z., Xu, H., Du, B., Lu, Y., Gao, H., Wu, J., & Chen, J. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3674838 · Cited by 67
A REVIEW ON DATA MINING IN BANKING SECTOR
Jayasree (2013) · American Journal of Applied Sciences · doi:10.3844/ajassp.2013.1160.1165 · Cited by 50
“The major part of implementation and care of credit risk management system (Raj and Portia, 2011) will be a typical data mining problem: the modeling of the credit instrument’s value through the default probabilities, recovery rates and rating migrations (Fung et al ., 2010).”
BackgroundUncertain time-series similarity
Dallachiesa, M., Nushi, B., Мирыленка, К., et al. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2350229.2350278 · Cited by 51
“In these applications privacy is a major concern, addressed by various privacy-preserving transforms [2, 11, 20], which introduce data uncertainty.”
Background
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
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.
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