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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Sensitive data disclosure is a long-standing challenge in protecting anonymity when data is published and/or analysed.
“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]
Much of the literature assumes a priori knowledge of the set of quasi-identifiers, or seeks to discover them on a case-by-case basis.
“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]
There is a tension between advancing the fairness state of the art, privacy, and policy.
“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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Publishing Sensitive Transactions for Itemset Utility
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Self-citationObtaining Information while Preserving Privacy: A Markov Perturbation Method for Tabular Data
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Privacy in Database Publishing
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Commutative Filters for Reducing Inference Threats in Multilevel Database Systems
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Privacy-preserving data mashup
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Self-citationNergiz, M. E., & Clifton, C. (2006) · doi:10.1109/icdew.2006.147 · Cited by 51 · see citing works
Inference Control in Statistical Databases, From Theory to Practice
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A Survey of Privacy-Preserving Methods Across Horizontally Partitioned Data
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A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data
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Privacy protection for RFID data
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Self-citationDisclosure avoidance practices and research at the U.S. Census Bureau: an update
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Privacy: a machine learning view
Vinterbo, S. A. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.31 · Cited by 39 · see citing works
On static and dynamic methods for condensation-based privacy-preserving data mining
Aggarwal, C. C., & Yu, P. S. (2008) · ACM Transactions on Database Systems · doi:10.1145/1331904.1331906 · Cited by 38 · see citing works
Self-citationHiding Location Information from Location-Based Services
Hengartner, U. (2007) · International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2007.56 · Cited by 37 · see citing works
Privacy Preserving Naive Bayes Classification
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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 · see citing works
Self-citationAsk a Better Question, Get a Better Answer A New Approach to Private Data Analysis
Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11965893_2 · Cited by 33 · see citing works
Integrating Private Databases for Data Analysis
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Self-citationNon-reversible privacy transformations
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Models and Methods for Privacy-Preserving Data Analysis and Publishing
Gehrke, J. E. (2006) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2006.100 · Cited by 26 · see citing works
A Fast Algorithm for Detecting Second Paths in Database Inference Analysis*
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FF-Anonymity: When Quasi-identifiers Are Missing
Wang, K., Xu, Y., Fu, A. W.-C., & Wong, R. C.-W. (2009) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2009.184 · Cited by 23 · see citing works
Self-citationOn Privacy-Preserving HistogramsPreprint
Chawla, S., Dwork, C., McSherry, F., et al. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.1371 · Cited by 21 · see citing works
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A framework for privacy-preserving cluster analysis
Fung, B. C. M., Wang, K., Wang, L., & Debbabi, M. (2008) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2008.4565028 · Cited by 18 · see citing works
Self-citationA framework for condensation-based anonymization of string data
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Self-citationA parallel solver for generalised additive models
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Distributed Data Mining Protocols for Privacy: A Review of Some Recent Results
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Cited by
Sorted by citations
Mobile Crowd Sensing and Computing
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015) · ACM Computing Surveys · doi:10.1145/2794400 · Cited by 723 · see citing works
“Therefore, new privacy-preserving approaches need to be developed to ensure that important information cannot be gleaned from mined patterns [Matatov et al. 2010; Fung et al. 2010; Christin et al. 2011].”
BackgroundAI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works
“PPDP objective is to preserve patients’ privacy while achieving the minimum possible loss of information [53].”
IoT Middleware: A Survey on Issues and Enabling technologies
Ngu, A. H. H., Gutiérrez, M., Metsis, V., Nepal, ., & Sheng, Q. Z. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2615180 · Cited by 685 · see citing works
“We refer readers to [58]–[60] for further information.”
BackgroundYin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638 · see citing works
Information Security in Big Data: Privacy and Data Mining
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. S. (2014) · IEEE Access · doi:10.1109/access.2014.2362522 · Cited by 636 · see citing works
Privacy in the Internet of Things: threats and challenges
Ziegeldorf, J. H., Morchon, O. G., & Wehrle, K. (2013) · Security and Communication Networks · doi:10.1002/sec.795 · Cited by 613 · see citing works
“Identity protection and, complementary, protection against identification is a predominant topic in RFID privacy (Section 3.1), but has also gained much attention in the areas of data anonymization [73, 74, 75], and privacy-enhancing identity management [76, 77].”
BackgroundInfluentialDifferential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564 · see citing works
“Anonymization techniques initiated from kanonymity, to l-diversity, and then to t-closeness method [54].”
MethodFairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
When Machine Learning Meets Privacy: A Survey and Outlook
Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., & Lin, Z. (2022) · ACM Computing Surveys · doi:10.1145/3436755 · Cited by 455 · see citing works
“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 Systematic Review of Re-Identification Attacks on Health Data
El Emam, K., Jonker, E., Arbuckle, L., & Malin, B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028071 · Cited by 427 · see citing works
“The availability of de-identified data has been critical for population health research, health services research, and public health. De-identification is the act of reducing the information content in data to decrease the probability of discovering an individual's identity. Over the past several decades, a number of…”
MethodPseudonym Schemes in Vehicular Networks: A Survey
Petit, J., Schaub, F., Feiri, M., & Kargl, F. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2345420 · Cited by 434 · see citing works
“[184] for details on k-anonymity, and its optimizations l-diversity and t-closeness.”
BackgroundPrivacy preservation in federated learning: An insightful survey from the GDPR perspective
Truong, N., Sun, K., Wang, S., Guitton, F., & Guo, Y. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102402 · Cited by 399 · see citing works
“ness will significantly depreciate reliability and usability of the dataset [33, 35, 40].”
BackgroundInfluentialDifferential 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
“A plethora of techniques have been proposed for privacy-preserving data publishing (see [1], [2] for surveys).”
BackgroundAnonymization of location data does not work
Zang, H., & Bolot, J. C. (2011) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2030613.2030630 · Cited by 367 · see citing works
“A good survey of privacy issues in data publishing can be found in [6].”
BackgroundWagner, I., & Eckhoff, D. (2018) · ACM Computing Surveys · doi:10.1145/3168389 · Cited by 409 · see citing works
“max), where 8y2P: minPr(y2D TjD P) max However, this model assumes that the data publisher and the adversary have access to the same external tables. This assumption may not hold in practice [Fung et al. 2010]. 5.5.5Hiding Property In communication systems, the source (or destination) hiding property measures the adve…”
BackgroundInfluentialDifferentially Private Data Publishing and Analysis: A Survey
Zhu, T., Li, G., Zhou, W., & Yu, P. S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2697856 · Cited by 330 · see citing works
Self-citationDifferentially 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
“Many algorithms have been proposed to preserve privacy, but only a few have considered the goal for classification [12].”
BackgroundSelf-citationOn the tradeoff between privacy and utility in data publishing
Li, T., & Li, N. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557079 · Cited by 318 · see citing works
“Several researchers have proposed to evaluate the utility of the anonymized data in terms of data mining workloads, such as classification and aggregate query answering (A comprehensive discussion on the privacy-preserving data publishing is given in [9]).”
BackgroundBig privacy: challenges and opportunities of privacy study in the age of big data
Yu, S. (2016) · IEEE Access · doi:10.1109/access.2016.2577036 · Cited by 307 · see citing works
“The mining community realized the privacy challenge decades ago, and extensive effort has been invested in privacy protection, such as privacy aware learning [9], [10], privacy preserving data publishing (PPDP) [11].”
BackgroundMethodInfluentialPrivacy-Preserving Data Mining: Methods, Metrics, and Applications
Mendes, R., & Vilela, J. P. (2017) · IEEE Access · doi:10.1109/access.2017.2706947 · Cited by 297 · see citing works
“Instead, different instantiations of the l-principle exist, differing on this particular definition [33],”
BackgroundInfluentialAnonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey
Majeed, A., & Lee, S. (2020) · IEEE Access · doi:10.1109/access.2020.3045700 · Cited by 297 · see citing works
“Fung et al. [31] study systematically summarized and evaluated different approaches used in the PPDP. Rajendran et al. [32] explained three prominent and most widely used anonymization models in a medical field, namely k -anonymity, (cid:96) -diversity, and t -closeness.”
MethodA Survey on Biometric Authentication: Toward Secure and Privacy-Preserving Identification
Zhang, R., & Yan, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2889996 · Cited by 279 · see citing works
Synthetic data generation for tabular health records: A systematic review
Hernandez, M., Epelde, G., Alberdi, A., Cilla, R., & Rankin, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.053 · Cited by 278 · see citing works
Protection of Big Data Privacy
Mehmood, A., Natgunanathan, I., Xiang, Y., Hua, G., & Guo, S. (2016) · IEEE Access · doi:10.1109/access.2016.2558446 · Cited by 281 · see citing works
“Many studies have been conducted to modify the data before publishing or storing them [38], [39] for further processing.”
BackgroundMethodInfluentialKifer, D., & Machanavajjhala, A. (2014) · ACM Transactions on Database Systems · doi:10.1145/2514689 · Cited by 270 · see citing works
“Because of this, the resulting privacy definitions tend to be less secure and are often subject to new kinds of attacks (see the surveys [Chen et al. 2009; Fung et al. 2010; Adam and Worthmann 1989] for more details). is also often not clear what data protecting individual records.”
BackgroundopenPDS: Protecting the Privacy of Metadata through SafeAnswers
de Montjoye, Y.-A., Shmueli, E., Wang, S. S., & Pentland, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098790 · Cited by 264 · see citing works
“The reader is referred to the surveys [43,44] for further details.”
BackgroundPrivacy-preserving trajectory data publishing by local suppression
Chen, R., Fung, B. C. M., Mohammed, N., Desai, B. C., & Wang, K. (2011) · Information Sciences · doi:10.1016/j.ins.2011.07.035 · Cited by 255 · see citing works
“Moreover, Fung et al. [8]pointoutthatifthetaxonomytreetendstobeflatandfansout,whichisthecaseoftrajectorydata,employinggeneralization loses moreinformationthanemployingsuppressionbecause generalization hasto mergeall siblings of a selected node to their parent node, whereas suppression only removes…”
Self-citationPrivacy in the Smart City—Applications, Technologies, Challenges, and Solutions
Eckhoff, D., & Wagner, I. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2748998 · Cited by 287 · see citing works
“For this reason, many variations of k-anonymity have been proposed, for example l-diversity which ensures that sensitive values are well-represented in each equivalence class [128], m-invariance which can deal with multiple data releases [129], or t-closeness which restricts the distribution of sensitive values [130]…”
BackgroundZhang, X., Yang, L. T., Liu, C., et al. (2013) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2013.48 · Cited by 241 · see citing works
International Journal Of Engineering Sciences & Research Technology
Holkar, J., & Fulmali, V. (2016) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.46534 · Cited by 235 · see citing works
Lo, B. (2015) · JAMA · doi:10.1001/jama.2015.292 · Cited by 232 · 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
“Due to the nature of high dimensionality in set-valued data, the extensive research on privacy-preserving data publishing (PPDP) for relational data does not fit well with set-valued data [13].”
BackgroundSelf-citationNetworking for Big Data: A Survey
Yu, S., Liu, M., Dou, W., Liu, X., & Zhou, S. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2610963 · Cited by 220 · see citing works
“In general, the data community realized the privacy issue in privacy preserving data publishing (PPDP) more than a decade ago [168], which followed by a new research topic of privacy aware learning [169].”
BackgroundTrajectory privacy in location-based services and data publication
Chow, C.-Y., & Mokbel, M. F. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2031331.2031335 · Cited by 209 · see citing works
“Such spatial and temporal attributes of a location trajectory can be considered as powerful quasi-identifiers that can be linked to various other kinds of physical data objects [19; 43].”
BackgroundSarwate, A. D., & Chaudhuri, K. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2259911 · Cited by 211 · see citing works
“There are many definitions and models for privacypreserving computation, and a recent survey by Fung et al. compares several different approaches [1].”
BackgroundA survey on data leakage prevention systems
Alneyadi, S., Sithirasenan, E., & Muthukkumarasamy, V. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.01.008 · Cited by 211 · see citing works
Valente, T. W., & Pitts, S. (2016) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031816-044528 · Cited by 205 · see citing works
Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges
Domingo‐Ferrer, J., Farràs, O., Ribes-González, J., & Sánchez, D. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.04.011 · Cited by 205 · see citing works
Publishing data from electronic health records while preserving privacy: A survey of algorithms
Gkoulalas-Divanis, A., Loukides, G., & Sun, J. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.06.002 · Cited by 203 · see citing works
“However, there are surveys in the computer science literature that do not focus on methods applicable to such data [39], as well as surveys that focus on privacy preservation methods for text data [94], privacy policies [91,93], or system security [36] issues.”
BackgroundData science and AI in FinTech: an overview
Cao, L., Yang, Q., & Yu, P. S. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00278-w · Cited by 238 · see citing works
Self-citationsharma (2018) · doi:10.1109/mic.2018.112102519 · Cited by 220 · see citing works
“Data anonymization methods [7] have been used for micro- data publishing (i.”
MethodTrust management of services in cloud environments
Noor, T. H., Sheng, Q. Z., Zeadally, S., & Yu, J. X. (2013) · ACM Computing Surveys · doi:10.1145/2522968.2522980 · Cited by 184 · see citing works
“, de-identification by removing personal identification information[Fung et al. 2010]) are inadequate in cloud environments [Roy et al.”
BackgroundTurner, J. A. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00137 · Cited by 176 · see citing works
“…methods resting state fMRI is non-invasive and does not expose subjects to radiation (i.e., PET), has higher spatial resolution (compared to EEG), and is easily applied in almost all clinical populations since it does not require participation in a cognitive task (i.e., task-driven fMRI).”
Differential privacy via wavelet transforms
Xiao, X., Wang, G., & Gehrke, J. E. (2010) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2010.5447831 · Cited by 178 · see citing works
Matejka, J., & Fitzmaurice, G. W. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025912 · Cited by 175 · see citing works
“As discussed by Govindaraju and Haslett [8] another use for datasets with the same statistical properties is the creation of “cloned” datasets to anonymize sensitive data [6].”
BackgroundDifferentially 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
“We refer readers to [5, 10, 3] for several up-to-date surveys.”
BackgroundMethodCyber security data sources for dynamic network research
Kent, A. D. (2016) · Security science and technology · doi:10.1142/9781786340757_0002 · Cited by 172 · see citing works
Comparing sets of patterns with the Jaccard index
Fletcher, S., & Islam, M. Z. (2018) · AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems · doi:10.3127/ajis.v22i0.1538 · Cited by 172 · see citing works
“The field of privacy-preserving data mining (Dwork 2008, Fung et al. 2010) is known for struggling with the inherent trade-off that must be made between the amount of privacy provided and the quality of the perturbed data (Fung et al. 2005, Nergiz & Clifton 2007).”
BackgroundCloudArmor: Supporting Reputation-Based Trust Management for Cloud Services
Noor, T. H., Sheng, Q. Z., Yao, L., Dustdar, S., & Ngu, A. H. H. (2015) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2015.2408613 · Cited by 171 · see citing works
“This layer consists of different cloud service providers who offer one or several cloud services, i.e., Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), publicly on the web (more details about cloud services models and designs can be found in [19]).”
BackgroundA Theory of Pricing Private Data
Li, C., Li, D. Y., Miklau, G., et al. (2014) · ACM Transactions on Database Systems · doi:10.1145/2691190.2691191 · Cited by 168 · see citing works
Privacy-Preserving Record Linkage for Big Data: Current Approaches and Research Challenges
Vatsalan, D., Sehili, Z., Christen, P., & Rahm, E. (2017) · Springer eBooks · doi:10.1007/978-3-319-49340-4_25 · Cited by 163 · see citing works
“The research areas of privacy-preserving data publishing [60] and statistical confidentiality [45] have been addressing these issues from different directions.”
BackgroundA Survey on Data Pricing: From Economics to Data Science
Pei, J. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3045927 · Cited by 171 · see citing works
“non-veriable information from a privacy-sensitive population [83]. There are many studies on preserving privacy in information goods. We refer interested readers to consult the rich body of surveys [8,35,60,70,76,194,205] and others. We do not discuss further details about general privacy preservation techniques in th…”
BackgroundContext-Aware Generative Adversarial Privacy
Huang, C., Kairouz, P., Chen, X., Sankar, L., & Rajagopal, R. (2017) · Entropy · doi:10.3390/e19120656 · Cited by 156 · see citing works
“Other annoymization techniques, such as generalization [11–13] and suppression [14–16], also cannot prevent an adversary from performing the sensitive linkages or recover private information from published datasets [17].”
BackgroundA rigorous and customizable framework for privacy
Kifer, D., & Machanavajjhala, A. (2012) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/2213556.2213571 · Cited by 154 · see citing works
“Because of this, the resulting privacy definitions tend to be less secure and are often subject to new kinds of attacks (see the surveys [6, 16, 1] for more details).”
BackgroundChevrier, R., Foufi, V., Gaudet-Blavignac, C., Robert, A., & Lovis, C. (2019) · Journal of Medical Internet Research · doi:10.2196/13484 · Cited by 150 · see citing works
Fast-adapting and privacy-preserving federated recommender system
Wang, Q., Yin, H., Chen, T., Yu, J., Zhou, A., & Zhang, X. (2021) · The VLDB Journal · doi:10.1007/s00778-021-00700-6 · Cited by 151 · see citing works
Guo, L., Yang, J., Peng, L., et al. (2014) · Computers in Industry · doi:10.1016/j.compind.2014.09.005 · Cited by 142 · see citing works
“Under certain privacy protection mechanisms [51,52,57], the fusion of these contextual information from multiple sources has great potential to be employed for improving the accuracy of the screening and automatic detection of ophthalmic diseases.”
BackgroundDifferentially private data cubes
Ding, B., Winslett, M., Han, J., & Li, Z. (2011) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1989323.1989347 · Cited by 141 · see citing works
“Compared to previous techniques for privacy-preserving data publishing (see [2, 15] for surveys), DP makes very conservative assumptions about the adversary’s background knowledge.”
BackgroundBalancing Privacy and Utility in Cross-Company Defect Prediction
Peters, F., Menzies, T., Gong, L., & Zhang, H. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2013.6 · Cited by 139 · see citing works
Graph Data Anonymization, De-Anonymization Attacks, and De-Anonymizability Quantification: A Survey
Ji, S., Mittal, P., & Beyah, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2633620 · Cited by 143 · see citing works
“[177] made a comprehensive survey on privacy preserving data publishing techniques for relational data,”
BackgroundA Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy
Goryczka, S., & Xiong, L. (2015) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2015.2484326 · Cited by 143 · see citing works
“Traditional approaches such as removing identifying attributes, generalizing, or perturbing individual attribute values, are susceptible to various attacks [26].”
BackgroundCentralized and Distributed Anonymization for High-Dimensional Healthcare Data
Mohammed, N., Fung, B. C. M., Hung, P. C. K., & Lee, C. K. (2010) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1857947.1857950 · Cited by 135 · see citing works
“RELATED WORK Data privacy has been an active area of research in statistics, database, and security communities for the last two decades [Adam and Wortman 1989; Fung et al. 2010].”
BackgroundSelf-citationA hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud
Zhang, X., Liu, C., Nepal, ., Yang, C., Dou, W., & Chen, J. (2014) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2014.02.007 · Cited by 133 · see citing works
Zhang, X., Liu, C., Nepal, ., Pandey, S., & Chen, J. (2012) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.238 · Cited by 132 · see citing works
“Although recent progress has been made in homomorphic encryption which theoretically allows performing computation on encrypted data sets, applying current algorithms are rather expensive due to their inefficiency [11].”
BackgroundHe, X. Q., Machanavajjhala, A., & Ding, B. (2014) · Proceedings - ACM-SIGMOD International Conference on Management of Data · doi:10.1145/2588555.2588581 · Cited by 131 · see citing works
Privacy in Internet of Things: From Principles to Technologies
Li, C., & Palanisamy, B. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2864168 · Cited by 130 · see citing works
“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 134 · see citing works
“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 · see citing works
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 122 · see citing works
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 · see citing works
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 118 · see citing works
“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 137 · see citing works
“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 115 · see citing works
“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 115 · see citing works
“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 113 · see citing works
“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 · see citing works
“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 113 · see citing works
“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 · see citing works
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 · see citing works
Secure multiple linear regression based on homomorphic encryption
Hall, R., Fienberg, S. E., & Nardi, Y. (2011) · Cited by 109 · see citing works
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 109 · see citing works
“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 109 · see citing works
“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 · see citing works
“[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 113 · see citing works
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 108 · see citing works
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 103 · see citing works
Near-optimal Differentially Private Principal Components
Chaudhuri, K., Sarwate, A. D., & Sinha, K. P. (2012) · Cited by 103 · see citing works
Learning Analytics: Principles and Constraints
Khalil, M., & Ebner, M. (2015) · doi:10.13140/rg.2.1.1733.2083 · Cited by 103 · see citing works
A Survey on Privacy in Social Media
Beigi, G., & Liu, H. (2020) · ACM/IMS Transactions on Data Science · doi:10.1145/3343038 · Cited by 139 · see citing works
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 · see citing works
“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., et al. (2011) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020599 · Cited by 101 · see citing works
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 98 · see citing works
“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 · see citing works
“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 106 · see citing works
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 95 · see citing works
“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 · see citing works
“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 · see citing works
Komishani, E. G., Abadi, M., & Deldar, F. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.11.007 · Cited by 94 · see citing works
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 · see citing works
“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 94 · see citing works
- 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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