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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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Items 401–500 of 1,001
Breaching Euclidean distance-preserving data perturbation using few known inputs
Giannella, C. R., Liu, K., & Kargupta, H. (2012) · Data & Knowledge Engineering · doi:10.1016/j.datak.2012.10.004 · Cited by 23
“erturbationmatrixis m×m (m is the numberof original data tuples), much bigger than Euclidean distance-preservingperturbation matrices, n ×n (n is the number of data dimensions). A survey: Fung et al. [34] provided a detailed survey of work related to this paper (using the descriptive term ”privacy-preserving data publ…”
A Differentially Private Random Decision Forest Using Reliable Signal-to-Noise Ratios
Fletcher, S., & Islam, M. Z. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26350-2_17 · Cited by 23
“Balancing the loss of information with the gain in privacy has been an active area of research for nearly two decades [11].”
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Ruggieri, S., Hajian, S., Kamiran, F., & Zhang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44851-9_44 · Cited by 23
“We follow the intriguing parallel between the role of the anti-discrimination authority in discrimination data analysis and the role of an attacker in privacypreserving data publishing [1,4,5] – an unauthorized (possibly malicious) entity.”
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“In our setting, certain assumptions also differ from the assumptions of prior work on privacy-preserving data publishing [13]: users do want to post information, but they should be aware of possible exposure and targeting risks.”
BackgroundJun, S., Lee, J., & Lee, J. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120258 · Cited by 23
Digital Forensics vs. Anti-Digital Forensics: Techniques, Limitations and RecommendationsPreprint
Yaacoub, J.-P. A., Noura, H., Salman, O., & Chehab, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.17028 · Cited by 23
Privacy-preserving aggregation in life cycle assessment
Kuczenski, B., Sahin, C., & Abbadi, A. E. (2016) · Environment Systems & Decisions · doi:10.1007/s10669-016-9620-7 · Cited by 22
“The publication is said to be ‘‘privacy-preserving’’ if a reader of the data, who is potentially an adversary, cannot discern any information that the provider regards as private (Fung et al. 2010).”
BackgroundHierarchical adaptive evolution framework for privacy-preserving data publishing
You, M., Ge, Y.-F., Wang, K., Wang, H., Cao, J., & Kambourakis, G. (2024) · World Wide Web · doi:10.1007/s11280-024-01286-z · Cited by 23
“Typically, G is used alongside a generalization hierarchy (illustrated in Figure 2), where each value corresponds to a generalization level in the taxonomy tree [11].”
Self-citationPrivacy-preserving publishing of opinion polls
Abdalaal, A., Nergiz, M. E., & Saygın, Y. (2013) · Computers & Security · doi:10.1016/j.cose.2013.03.008 · Cited by 22
Location Privacy for Vehicle-to-Grid Interaction through Battery Management
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SaC‐FRAPP: a scalable and cost‐effective framework for privacy preservation over big data on cloud
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“As to privacy models and protection techniques, privacy-preserving data publishing research community has extensively investigated on the issues and made fruitful progress with a variety of privacy models, privacy preserving methods, and algorithms [7].”
BackgroundMethodInfluentialPrivacy preserving visualization for social network data with ontology information
Chou, J., Bryan, C., & Ma, K. (2017) · IEEE Pacific Visualization Symposium · doi:10.1109/pacificvis.2017.8031573 · Cited by 22
“The term for this type of action is generalization, or alternatively, suppression [12].”
BackgroundQuery-Based Linked Data Anonymization
Delanaux, R., Bonifati, A., Rousset, M.-C., & Thion, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00671-6_31 · Cited by 22
“Privacy preserving data publishing (PPDP) has been a long-standing research goal for several research communities, as witnessed by a flurry of work on the topic [7].”
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“Therefore, considering privacy-preserving data release technology [11] becomes necessary.”
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“For example, in the medical field, the privacy is more related to provider information, and thus, removing the provider information may increase privacy, but severely affects utility [10] [11].”
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“[10] provided a survey on state-of-the-art anonymization techniques, which can be divided into the following categories: Suppression, generalization, anatomization, permutation and perturbation.”
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Michele Bezzi, A. (2010) · Cited by 22
Qian, J., Zheng, M., Yu, Y., Zhou, C., & Miao, D. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121316 · Cited by 24
The effect of homogeneity on the computational complexity of combinatorial data anonymization
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A general framework for managing and processing live video data with privacy protection
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A hybrid-security model for privacy-enhanced distributed data mining
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“For a detailed overview and references, see (Fung et al. 2010).”
BackgroundMethodAn enhanced l-diversity privacy preservation
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“The model works by ensuring that each record of a table is identical to at least k - 1 other record with respect to quasi-identifiers (QI), which could be potentially used to identify individuals by linking these attributes to external data sets [5].”
BackgroundA Novel Differential Privacy Approach that Enhances Classification Accuracy
Zaman, A., Obimbo, C., & Dara, R. A. (2016) · doi:10.1145/2948992.2949027 · Cited by 21
“In literature, it is also found that many privacy preserving models suffer from table linkage, record linkage, attribute linkage, and probabilistic attacks [14].”
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Luo, W., Jiang, H., & Zhao, D. (2017) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2017.2669083 · Cited by 21
“Although many works have been proposed on the preservation of privacy [19]–[24], to the best of our knowledge, no research has been conducted on preserving the privacy of a purchaser’s rating rank.”
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Natarajan, R., & Selvakumar, A. A. L. (2018) · Cluster Computing · doi:10.1007/s10586-018-1827-6 · Cited by 21
“[11] suggested that in the information realm, loss of privacy is usually associated with failure to control access to information, to control the flow of information, or to control the purposes for which information is employed.”
Privacy Characterization and Quantification in Data Publishing
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“t-closeness also has several other limitations [29].”
An information-aware visualization for privacy-preserving accelerometer data sharing
Xiao, F., Lu, M., Zhao, Y., Menasria, S., Meng, D., Xie, S., Li, J., & Li, C. (2018) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-018-0137-6 · Cited by 19
Privacy Preserving Visualization: A Study on Event Sequence Data
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T-Closeness Slicing: A New Privacy-Preserving Approach for Transactional Data Publishing
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Privacy risk assessment in context: A meta-model based on contextual integrity
Henriksen-Bulmer, J., Faily, S., & Jeary, S. (2019) · Computers & Security · doi:10.1016/j.cose.2019.01.003 · Cited by 22
Publishing Sensitive Trajectory Data Under Enhanced l-Diversity Model
Yao, L., Wang, X., Wang, X., Hu, H., & Wu, G. (2019) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2019.00-61 · Cited by 21
Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level
Xiao, Y., & Li, H. (2020) · Information · doi:10.3390/info11030166 · Cited by 21
Privacy-Preserving Spatio-Temporal Patient Data Publishing
Olawoyin, A. M., Leung, C. K.-S., & Choudhury, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59051-2_28 · Cited by 21
Decision Tree Classification with Differential Privacy: A SurveyPreprint
Fletcher, S., & Islam, M. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01919 · Cited by 21
“In other words, their definition prevents an individual’s values being discovered (attribute linkage), but not whether or not the individual is in the dataset (table linkage) [Fung et al. 2010].”
InfluentialSadat, M. N., Aziz, M. A., Mohammed, N., Chen, F., Wang, S., & Jiang, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.02577 · Cited by 21
“This issue can be resolved by some of the query privacy or private information retrieval techniques [40], [41], [42].”
BackgroundData Protection in Healthcare Social Networks
Li, J. (2013) · IEEE Software · doi:10.1109/ms.2013.99 · Cited by 20
Global Combination and Clustering Based Differential Privacy Mixed Data Publishing
Chen, L., Zeng, L., Mu, Y., & Chen, L. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3237822 · Cited by 21
Towards a data privacy-predictive performance trade-off
Carvalho, T., Moniz, N., Faria, P., & Antunes, L. F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119785 · Cited by 26
Structural Diversity for Resisting Community Identification in Published Social Networks
Tai, C.-H., Yu, P. S., Yang, D.-N., & Chen⋆, M.-S. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.40 · Cited by 20
“To prevent community identification in published social networks by degree attacks, therefore, we proposek- structural diversity, which ensures that for each vertex, there are other vertices with the same degree located in at leastk − 1 different communities.”
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Babu, K. S., Reddy, N. S., Kumar, N., et al. (2013) · Research Explorer (The University of Manchester) · doi:10.5555/2612156.2612157 · Cited by 20
“3 Related Work Several algorithms have been developed with the purpose of making de-identified data k-anonymous [16, 17, 18, 19].”
Privacy Preservation in the Internet of Things
Berrehili, F. Z., & Belmekki, A. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-1627-1_13 · Cited by 20
Information Credibility Modeling in Cooperative Networks: Equilibrium and Mechanism Design
Jiang, C., Kuang, L., Han, Z., et al. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2659078 · Cited by 20
“We developed a Java program based on the open source anonymization framework ARX of [46], which supports different types of privacy criteria and provides multiple methods for quantifying the value of information after anonymization and mining [47].”
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“Balancing privacy and utility in privacy preserving data publishing (PPDP) is a NP-hard problem [15–20].”
BackgroundPrivacy in Social Media: Identification, Mitigation and ApplicationsPreprint
Beigi, G., & Liu, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.02191 · Cited by 20
“. 9, No. 4, Article 39. Publication date: July 2018. Privacy in Social Media 39:7 2.3 Related Work There are multiple relevant surveys related to the privacy of data and privacy preserving approaches [1, 5, 50, 55, 78, 83, 157, 163, 175, 194]. Fung et al. [55] reviews privacy preserving data publishing methods for rel…”
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Ruggieri, S. (2015) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 19
Chen, C., Hu, X., Yang, L., et al. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3309783 · Cited by 19
“As pointed out in [10], data in its original form, however, typically contains sensitive information about individuals, and the publication of such data will violate individual privacy.”
BackgroundDifferentially private enhanced permissioned blockchain for private data sharing in industrial IoT
Islam, M. N., Rehmani, M. H., & Chen, J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119997 · Cited by 19
Privacy Preserving Data Publishing for Recommender System
Chen, X., & Huang, V. (2012) · IEEE Annual Computer Software and Applications Conference Workshops (COMPSACW) · doi:10.1109/compsacw.2012.33 · Cited by 19
“A Simpli ed Model of PPDP privacy has become an important concern.”
BackgroundInfluentialUsing Feature Selection to Improve the Utility of Differentially Private Data Publishing
Jafer, Y., Matwin, S., & Sokolova, M. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.08.076 · Cited by 19
Gkoulalas-Divanis, A., & Loukides, G. (2011) · doi:10.1145/1971690.1971695 · Cited by 19
“While data produced by perturbation can be used to build accurate data mining models, they cannot be analyzed at a record level, which is crucial in several applications, such as biomedical analysis [9, 22].”
BackgroundPrivacy-Preserving Layer over MapReduce on Cloud
Zhang, X., Liu, C., Nepal, ., Dou, W., & Chen, J. (2012) · International Conference on Cloud and Green Computing · doi:10.1109/cgc.2012.43 · Cited by 19
“In such cases, encryption or access control mechanisms alone fails to ensure privacy preservation and data utility exposure.”
BackgroundYour trajectory privacy can be breached even if you walk in groups
Sui, K., Zhao, Y., Liu, D., Ma, M., Xu, L., Zimu, L., & Pei, D. (2016) · International Workshop on Quality of Service · doi:10.1109/iwqos.2016.7590444 · Cited by 19
“Among several privacy risks [13], we study a typical one — sensitive attribute disclosure .”
BackgroundBRAINS (Brain Imaging in Normal Subjects) Expert Working Group, Shenkin, S. D., Pernet, C. R., Nichols, T. E., Poline, J., Matthews, P. M., van der Lugt, A., Mackay, C. E., Lanyon, L., Boardman, J. P., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.030 · Cited by 19
“…or imaging data could identify repeat subjects if enough data were available – however this approach may not be reliable, and also implies that even de-identified data about an individual cannot be considered truly anonymous, as data may be susceptible to data linkage attacks (Fung et al., 2010).”
BackgroundA privacy-preserving approach for multimodal transaction data integrated analysis
Sui, P., & Li, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.09.130 · Cited by 19
“Subsequently, aimed at improving shortcomings of k-anonymity, other privacy models such as l-diversity, t-closeness have emerged [3].”
BackgroundLocation Privacy-Preserving Data Recovery for Mobile Crowdsensing
Zhou, T., Cai, Z., Xiao, B., et al. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3264961 · Cited by 19
“Data privacy/security has received considerable attention in many fields, specially database management [12] and MCS [8].”
BackgroundAnonymous Privacy Preservation Based on m-Signature and Fuzzy Processing for Real-Time Data Release
Le, J., Zhang, D., Mu, N., Liao, X., & Yang, F. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2872902 · Cited by 19
“The two main scenarios of dynamic data release on an underlying table include [26]: 1) sequential publishing [27], [28], where different (vertical) projections of an underlying table on different subsets of attributes are released in a sequential manner and 2) continuous data publishing [7], [8],…”
BackgroundClustering-oriented privacy-preserving data publishing
Ni, W., & Chong, Z. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.05.012 · Cited by 19
A Differentially Private Approach for Querying RDF Data of Social Networks
Silva, R. R. C., Leal, B. C., Brito, F. T., et al. (2017) · International Database Engineering & Applications Symposium (IDEAS) · doi:10.1145/3105831.3105838 · Cited by 19
“Efforts have been dedicated to deal with the problem of applying Differential Privacy over RDF data [4, 14, 17].”
MethodUsing mobile phone data to evaluate access to essential services following natural hazards
Swanson, T., & Guikema, S. D. (2023) · Risk Analysis · doi:10.1111/risa.14201 · Cited by 20
“…for enacting and upholding policies and practices that protect privacy of individual users, including de-identification that prohibits re-identification, safe data sharing protocols, and only publishing results in aggregate (Fung et al., 2010; National Institute of Standards and Technology, 2020).”
BackgroundPrivacy preserving data publishing of categorical data through k ‐anonymity and feature selection
Aristodimou, A., Antoniades, A., & Pattichis, C. S. (2016) · Healthcare Technology Letters · doi:10.1049/htl.2015.0050 · Cited by 18
Raju, N. V. S. L., Seetaramanath, M. N., & Rao, P. S. (2019) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2019.04.03 · Cited by 18
“Published data utility gain can be measured by the trade-off metrics, which can find out the privacy loss and information gain for each generalization or suppression activity [3].”
BackgroundPrivacy-aware data cleaning-as-a-service
Huang, Y., Milani, M., & Chiang, F. (2020) · Information Systems · doi:10.1016/j.is.2020.101608 · Cited by 18
“The generalization problem has been shown to be intractable, where optimization, and approximation algorithms have been proposed (cf. [11] for a survey).”
BackgroundInfluentialPrivacy Preserving Parallel Clustering Based Anonymization for Big Data Using MapReduce Framework
Lawrance, J. U., & Nayahi, J. J. V. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.1987709 · Cited by 18
“Privacy-preserving data publishing (PPDP) is a concept that provides various tools and techniques for preserving data privacy while publishing the data over the Internet (Aggarwal and Yu, 2008; Fung et al. 2010; Fahad et al. 2014).”
BackgroundReal-world K-Anonymity applications: The KGen approach and its evaluation in fraudulent transactions
Pascale, D. D., Cascavilla, G., Tamburri, D. A., et al. (2023) · Information Systems · doi:10.1016/j.is.2023.102193 · Cited by 18
Kim, J. W., & Jang, B. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103951 · Cited by 20
MAGE: A semantics retaining K-anonymization method for mixed data
Han, J., Yu, J., Mo, Y., Lu, J., & Liu, H. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.10.009 · Cited by 18
Database Fragmentation with Confidentiality Constraints
Xu, X., Xiong, L., & Liu, J. (2015) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/2699026.2699121 · Cited by 18
“The literature following the seminal work on k -anonymity [16, 17] adopts syntactic privacy notions by considering spe-cific attacks and assuming the attacker has limited background knowledge [10].”
BackgroundPPTP: Privacy-Preserving Traffic Padding in Web-Based Applications
Liu, W. M., Wang, L., Cheng, P., Ren, K., Zhu, S., & Debbabi, M. (2014) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2014.2302308 · Cited by 18
Privacy challenges in Ambient Intelligence systems
Caire, P., Moawad, A., Efthymiou, V., Bikakis, A., & Traon, Y. L. (2016) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-160405 · Cited by 38
“Fung’s et al. survey [56] provides a typical scenario for data collection and publishing (described in Fig.”
Partial k-Anonymity for Privacy-Preserving Social Network Data Publishing
Liu, P., Bai, Y. Z., Wang, L., et al. (2016) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194017500048 · Cited by 18
A Survey on Privacy Preserving Data Mining Techniques
Taric, G. J., & Poovammal, E. (2017) · Indian Journal of Science and Technology · doi:10.17485/ijst/2017/v10i5/111138 · Cited by 18
Achieving Privacy Preservation Constraints in Missing-Value Datasets
Riyana, S., Nanthachumphu, S., & Riyana, N. (2020) · SN Computer Science · doi:10.1007/s42979-020-00241-9 · Cited by 18
“One of the vital privacy violation issues occurred in rating datasets, i.e, it is when the Netflix recommendation system to be improved in 2006 [8, 10].”
BackgroundComputing Compliant Anonymisations of Quantified ABoxes w.r.t. $$\mathcal {EL} $$ Policies
Baader, F., Kriegel, F., Nuradiansyah, A., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62419-4_1 · Cited by 18
Blockchain Empowered Differentially Private and Auditable Data Publishing in Industrial IoT
Xu, L., Bao, T., & Zhu, L. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3045038 · Cited by 18
Game theoretic data privacy preservation: Equilibrium and pricing
Xu, L., Jiang, C., Wang, J., et al. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7249454 · Cited by 18
“How to make a trade-off between privacy and utility is a fundamental issue for PPDP.”
BackgroundInfluentialFung, B. C. M., Al-Hussaeni, K., & Cao, M. (2009) · IEEE International Conference on RFID · doi:10.1109/rfid.2009.4911184 · Cited by 18
“The database community has spent lots of effort on privacypreserving data publishing [ 7 ], where the goal is to transform relational data into an anonymous version for preventing record and attribute linkages.”
BackgroundSelf-citationA Dummy-Based Approach for Preserving Source Rate Privacy
Diyanat, A., Khonsari, A., & Shariatpanahi, S. P. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2515050 · Cited by 18
“2515050 the focus of attention of research studies during the past few years [1], [2].”
BackgroundI Am Not What I Write: Privacy Preserving Text Representation LearningPreprint
Beigi, G., Shu, K., Guo, R., Wang, S., & Liu, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03189 · Cited by 18
“niques are inefficient for user-generated textual data because this data is highly unstructured, noisy and unlike traditional documental content, consists of large numbers of short and informal posts [25]. Moreover, these works may impose a significant utility loss for protecting textual data as they may not explicitl…”
BackgroundYan, Z., & Chakraborty, D. (2014) · Synthesis lectures on data, semantics and knowledge · doi:10.1007/978-3-031-79453-7 · Cited by 17
Clarkson, M. R., & Schneider, F. B. (2014) · Mathematical Structures in Computer Science · doi:10.1017/s0960129513000595 · Cited by 17
Reidentification Risk in Panel Data: Protecting fork-Anonymity
Li, S., Schneider, M. J., Yu, Y., & Gupta, S. (2022) · Information Systems Research · doi:10.1287/isre.2022.1169 · Cited by 20
An evaluation on big data generalization using k-Anonymity algorithm on cloud
Kavitha, S., Yamini, S., & Raja Vadhana, P. (2015) · International Conference on Intelligent Systems and Control · doi:10.1109/isco.2015.7282237 · Cited by 17
“A "cut" is formed through the domain values of an attribute [4].”
BackgroundMethodMET𝔸P: revisiting Privacy-Preserving Data Publishing using secure devices
Allard, T., Nguyen, B., & Pucheral, P. (2013) · Distributed and Parallel Databases · doi:10.1007/s10619-013-7122-x · Cited by 17
“Although a lot of work has been done on privacy models trying to reach the best utility/privacy trade-off of the sanitized dataset (see [13, 18] for two surveys), much less effort has been spent on the practical implementation and deployment of a PPDP process.”
BackgroundA logical framework for privacy-preserving social network publication
Hsu, T., Liau, C., & Wang, D. (2014) · Journal of Applied Logic · doi:10.1016/j.jal.2013.12.001 · Cited by 18
Towards Self-Awareness Privacy Protection for Internet of Things Data Collection
Wong, K., & Kim, M. H. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/827959 · Cited by 17
“A survey of recent attacks and privacy models in data publishing can be found in [30].”
BackgroundWaveCluster with Differential Privacy
Chen, L., Yu, T., & Chirkova, R. Y. (2015) · doi:10.1145/2806416.2806546 · Cited by 17
“that PrivTHR and PrivTHR EMachieve high utility for both types of utility measures, and are superior to Baseline and PrivQT. 2. RELATED WORK The syntactic approaches for privacy preserving clustering [18] is to output k-anonymous clusters. Friedman et al. [17] presented an algorithm to output k-anonymous clusters by u…”
MethodA Survey on Privacy Preserving Data Publishing
Gokila, S., & Venkateswari, P. (2014) · International Journal on Cybernetics & Informatics · doi:10.5121/ijci.2014.3101 · Cited by 17
Rajaei, M., Haghjoo, M. S., & Miyaneh, E. K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130693 · Cited by 17
Enhancing utility and privacy with noisy minimax filters
Hamm, J. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953386 · Cited by 17
Scene Understanding For Real Time Processing Of Queries Over Big Data Streaming Video
Aved, A. J. (2013) · Journal of International Crisis and Risk Communication Research · Cited by 17
k-DDD Measure and MapReduce Based Anonymity Model for Secured Privacy-Preserving Big Data Publishing
Madan, S., & Goswami, P. (2019) · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488519500089 · Cited by 17
Datenschutz und Datensicherheit in Digital Public Health
Kunz, T., Lange, B., & Selzer, A. (2020) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-019-03083-w · Cited by 17
Heterogeneous data release for cluster analysis with differential privacy
Wang, R., Fung, B. C. M., & Zhu, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106047 · Cited by 17
Self-citationLocal generalization and bucketization technique for personalized privacy preservation
Li, B., & He, K. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.12.008 · Cited by 17
“The goal of preventing such privacy disclosures has resulted in the development of many anonymization techniques (see survey [3]).”
BackgroundPrivacy in the Cloud: A Survey of Existing Solutions and Research Challenges
Silva, P. V. S., Monteiro, E., & Simões, P. (2021) · IEEE Access · doi:10.1109/access.2021.3049599 · Cited by 21
“A survey on privacy-preserving data publishing by Fung et al. [13] presents a consistent review of anonymization algorithms, metrics, and different publishing scenarios.”
BackgroundMethodSketching Data Sets for Large-Scale Learning: Keeping only what you need
Gribonval, R., Chatalic, A., Keriven, N., et al. (2021) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3092574 · Cited by 17
“A common way to preserve privacy is to have a trusted dataholder (or “curator”) corrupt the response to each query of the dataset [45] in a controlled manner.”
Influential
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.
Statistics and arithmetic are recomputed, not judged.
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