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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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“corresponding data subject so that the sensitive attributes cannot be linked with specific individuals [70].”
BackgroundMarketing ecosystem: An outside-in view for sustainable advantage
Zhang, J. Z., & Watson, G. F. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2020.04.023 · Cited by 149
“This places emphasis on privacy-preserving data publishing (PPDP), which represents data stewardship that balances privacy preservation with utility loss (Fung, Wang, Chen, & Yu, 2010).”
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646
A near-optimal algorithm for differentially-private principal components
Chaudhuri, K., Sarwate, A. D., & Sinha, K. P. (2013) · doi:10.5555/2567709.2567754 · Cited by 120
A taxonomy of open government data research areas and topics
Charalabidis, Y. K., Alexopoulos, C., & Loukis, E. (2016) · Journal of Organizational Computing and Electronic Commerce · doi:10.1080/10919392.2015.1124720 · Cited by 121
Tu, Z., Zhao, K., Xu, F., Li, Y., Su, L., & Jin, D. (2018) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2018.2877790 · Cited by 124
“However, it fails to provide protection against probabilistic attack [23] which means the attacker can still acquire private information without re-identifying the user.”
BackgroundDecision Tree Classification with Differential Privacy
Fletcher, S., & Islam, M. Z. (2019) · ACM Computing Surveys · doi:10.1145/3337064 · Cited by 154
“Many strategies for protecting privacy have been explored in recent years (Adam and Worthmann 1989; Aggarwal and Yu 2008; Fung et al. 2010).”
Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects
Malik, M. B., ghazi, M., & Ali, R. (2012) · International Conference on Computer and Communication Technology · doi:10.1109/iccct.2012.15 · Cited by 118
“Perturbation has a long back history, being used in statistical disclosure control as it has an inherent property of simplicity, efficiency and ability to preserve statistical information [24].”
BackgroundAutomatic Archiving versus Default Deletion
Xu, B., Chang, P. F., Welker, C. L., Bazarova, N. N., & Cosley, D. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2819948 · Cited by 125
“This is sometimes proposed as a privacy-preserving mechanism in the database domain [12], but is largely unexplored in user interfaces.”
BackgroundTowards an axiomatization of statistical privacy and utility
Kifer, D., & Lin, B.-R. (2010) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/1807085.1807106 · Cited by 114
“For surveys on statistical privacy, see [10, 21 , 1]. The need for a better understanding of privacy denitions and privacy mechanisms was underscored by the work of Dinur and Nissim [12] (and later by Dwork et al. [17]) that showed that some intuitive methods for preserving privacy actually did not preserve privacy ac…”
BackgroundPrivacy or Utility in Data Collection? A Contract Theoretic Approach
Xu, L., Jiang, C., Chen, Y., Ren, Y., & Liu, K. J. R. (2015) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2015.2425798 · Cited by 114
“I. INTRODUCTION”
Zhang, X., Dou, W., Pei, J., Nepal, ., Yang, C., Liu, C., & Chen, J. (2014) · IEEE Transactions on Computers · doi:10.1109/tc.2014.2360516 · Cited by 114
“Since the -dissimilarity satisfiability problem is proved to be NP-hard in [12], the conclusion holds as well for the -dissimilarity satisfiability problem.”
BackgroundInfluentialGeometric data perturbation for privacy preserving outsourced data mining
Chen, K., & Liu, L. (2010) · Knowledge and Information Systems · doi:10.1007/s10115-010-0362-4 · Cited by 112
Differentially private consensus algorithm for continuous-time heterogeneous multi-agent systems
Liu, X., Zhang, J., & Wang, J. (2020) · Automatica · doi:10.1016/j.automatica.2020.109283 · Cited by 112
Secure multiple linear regression based on homomorphic encryption
Hall, R., Fienberg, S. E., & Nardi, Y. (2011) · Cited by 109
DPPro: Differentially Private High-Dimensional Data Release via Random Projection
Xu, C., Ren, J., Zhang, Y., Qin, Z., & Ren, K. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737966 · Cited by 112
“The aim of PPDP is to release some manipulated sensitive data such that the released data can still be used for the intended purposes, but the privacy of the individuals in the database is preserved.”
BackgroundInfluentialMohammed, N., Fung, B. C. M., Hung, P. C. K., & Lee, C.-K. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557157 · Cited by 110
“In fact, alternative techniques, such as privacypreserving data mining and data publishing [2][7], are available to them provided that the data mining quality does not significantly degrade.”
BackgroundSelf-citationA cloud-edge based data security architecture for sharing and analysing cyber threat information
Chadwick, D. W., Fan, W., Costantino, G., de Lemos, R., Cerbo, F. D., Herwono, I., Manea, M., Mori, P., Sajjad, A., & Wang, X.-S. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.06.026 · Cited by 107
“[8] proposed the privacy-preserving data publishing approach in order to optimize the trade-off between data utility and privacy.”
MethodPrivacy in Social Networks: A Survey
Zheleva, E., & Getoor, L. C. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8462-3_10 · Cited by 119
Privacy-Preserving Learning Analytics: Challenges and Techniques
Gürsoy, M. E., İnan, A., Nergiz, M. E., & Saygın, Y. (2016) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2016.2607747 · Cited by 113
Towards Privacy for Social Networks: A Zero-Knowledge Based Definition of Privacy
Gehrke, J. E., Lui, E., & Pass, R. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19571-6_26 · Cited by 108
Near-optimal Differentially Private Principal Components
Chaudhuri, K., Sarwate, A. D., & Sinha, K. P. (2012) · Cited by 104
Learning Analytics: Principles and Constraints
Khalil, M., & Ebner, M. (2015) · doi:10.13140/rg.2.1.1733.2083 · Cited by 103
A Survey on Privacy in Social Media
Beigi, G., & Liu, H. (2020) · ACM/IMS Transactions on Data Science · doi:10.1145/3343038 · Cited by 153
A Review of Privacy-Preserving Federated Learning for the Internet-of-Things
Briggs, C., Fan, Z., & Andras, P. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-70604-3_2 · Cited by 101
“The privacy preserving methods discussed in this section can be described as either suppressive or perturbative [75].”
BackgroundMethodPrivacy-preserving social network publication against friendship attacks
Tai, C.-H., Yu, P. S., Yang, D.-N., & Chen⋆, M.-S. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020599 · Cited by 106
Self-citationPrivacy Preservation in Big Data From the Communication Perspective—A Survey
Wang, T., Zheng, Z., Rehmani, M. H., Yao, S., & Huo, Z. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2865107 · Cited by 99
“As they can be adopted for different purposes of privacy preservaton, privacy-preserving models can fall into several classes, with PPDP [11] and PPDM [35] attracting substan-”
BackgroundInfluentialWhen Machine Learning Meets Privacy: A Survey and OutlookPreprint
Liu, B., Ding, M., Shaham, S., Rahayu, W. J., Farokhi, F., & Lin, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.11819 · Cited by 99
“n sharing [82, 142], and cryptographic mechanisms [43, 127] are the major technologies. However, the traditional privacy protection schemes focus on structured data, such as an entry in the databases [162]. With the introduction of new applications such as Internet of Things (IoT) and vehicular networks, both the volu…”
BackgroundA Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis
Wang, T., Zhang, X., Feng, J., & Yang, X. (2020) · Sensors · doi:10.3390/s20247030 · Cited by 116
GARP-face: Balancing privacy protection and utility preservation in face de-identification
Du, L., Yi, M., Blasch, E., & Ling, H. (2014) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2014.6996249 · Cited by 96
“For a survey of privacy protection, one can refer to [6].”
BackgroundCollusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel
Jung, T., Li, X., & Wan, M. (2014) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2014.2309134 · Cited by 95
“Privacy-preserving statistics calculation is desired in various applications [36], [37], [38].”
BackgroundCorrelated Differential Privacy: Feature Selection in Machine Learning
Zhang, T., Zhu, T., Xiong, P., Huo, H., Tari, Z., & Zhou, W. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2936825 · Cited by 95
Komishani, E. G., Abadi, M., & Deldar, F. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.11.007 · Cited by 94
Anonymization of Sensitive Quasi-Identifiers for l-Diversity and t-Closeness
Sei, Y., Okumura, H., Takenouchi, T., & Ohsuga, A. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2698472 · Cited by 94
“The value of c 1 [1] and c 1 [2] is Artist and Fever, respectively.”
BackgroundSurvey on Improving Data Utility in Differentially Private Sequential Data Publishing
Yang, X., Wang, T., Ren, X., & Yu, W. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2715334 · Cited by 93
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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