The DOI matches its Crossref record; 154 of the 156 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
(1992) · Cited by 23,431
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11787006_1 · Cited by 6,125
Untraceable electronic mail, return addresses, and digital pseudonyms
Chaum, D. (1981) · Communications of the ACM · doi:10.1145/358549.358563 · Cited by 4,299
L -diversity: Privacy beyond k -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617
Differential Privacy: A Survey of Results
Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480775 · Cited by 3,261
Research works printed but not linked: 23
- 10.5555/1083592.1083696 · DOI
- Chawla S., Proceedings of the Uncertainty in Artificial Intelligence Coference (UAI).
- Dalenius T., 1986, Finding a needle in a haystack - or identifying anonymous census record, J. Official Statistics, 2, 329
- Dalenius T., 1977, Towards a methodology for statistical disclosure control, Statistik Tidskrift, 15, 429
- 10.5555/1791834.1791836 · DOI
- Dwork C., Proceedings of the 24th International Cryptology Conference (CRYPTO). 528--544
- Fuller W. A., 1993, Masking procedures for microdata disclosure limitation, Official Statistics, 9, 383
- Fung B. C. M., Proceedings of the 2008 IEEE International Conference on Intelligence and Security Informatics (ISI). 46--51
- Hundepool A., Proceedings of the 3rd International Seminar on Statistical Confidentiality.
- Jajodia S. and Meadows C. 1995. Inference problems in multilevel database management systems. In IEEE Information Security: An Integrated Collection of Essays. 570--584. Jajodia S. and Meadows C. 1995. Inference problems in multilevel database management systems. In IEEE Information Security: An Integrated Collection of Essays. 570--584.
- Kim J., Proceedings of the ASA Section on Survey Research Methods. 114--119
- Li N., Proceedings of the 21st IEEE International Conference on Data Engineering (ICDE).
- Martin D., Proceedings of the 23rd IEEE International Conference on Data Engineering (ICDE).
- Motwani R., Proceedings of the Conference on Very Large Data Bases (VLDB).
- Nergiz M. E., Proceedings of the 23rd International Conference on Data Engineering (ICDE). 1417--1421
- Papadimitriou S., Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB), 459--470
- Rastogi V., Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB). 531--542
- 10.5555/646115.679937 · DOI
- Wang K., Proceedings of the 4th IEEE International Conference on Data Mining (ICDM).
- Wong R. C. W., Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB). 543--554
- Xiao X., Proceedings of the 32nd Conference on Very Large Data Bases (VLDB).
- Yao C., Proceedings of the 31st Conference on Very Large Data Bases (VLDB). 910--921
- Zhang Q., Proceedings of the 23rd IEEE International Conference on Data Engineering (ICDE).
Cited by
Sorted by citations
Items 701–800 of 1,001
Aligning Models and Data for Systemic Risk Analysis
Stein, R. M. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139151184.004 · Cited by 9
Structural Attack to Anonymous Graph of Social Networks
Zhu, T., Wang, S., Li, X., et al. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/237024 · Cited by 9
The l -Diversity problem: Tractability and approximability
Dondi, R., Mauri, G., & Zoppis, I. F. (2012) · Theoretical Computer Science · doi:10.1016/j.tcs.2012.05.024 · Cited by 9
Privacy beyond sensitive values
Huang, X., Liu, J., Han, Z., et al. (2015) · Science China Information Sciences · doi:10.1007/s11432-014-5232-3 · Cited by 9
Bredereck, R., Nichterlein, A., & Niedermeier, R. (2013) · Algorithms · doi:10.3390/a6040678 · Cited by 9
Determining t in t-closeness using Multiple Sensitive Attributes
Roy, D., & Jena, S. K. (2013) · International Journal of Computer Applications · doi:10.5120/12179-8291 · Cited by 9
Privacy Threat Analysis of Social Network Data
Ninggal, M. I. H., & Abawajy, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24669-2_16 · Cited by 9
“The conventional methods proposed for micro data privacy [7], cannot be used directly to ensure privacy of social network data due to complex dependencies between the data and various relationships.”
MethodEnhancing Privacy of Information Brokering in Smart Districts by Adaptive Pseudonymization
Suomalainen, J., & Julku, J. (2016) · IEEE Access · doi:10.1109/access.2016.2538818 · Cited by 10
“There is a huge body of work that considers privacypreserving data-publishing [17], i.”
BackgroundPrivacy preserving data mining on published data in healthcare: A survey
Abuwardih, L. A., Shatnawi, W., & AlEroud, A. F. (2016) · doi:10.1109/csit.2016.7549444 · Cited by 9
Partition Based Perturbation for Privacy Preserving Distributed Data Mining
Sheela, M. A., & Vijayalakshmi, K. (2017) · Cybernetics and Information Technologies · doi:10.1515/cait-2017-0015 · Cited by 9
“This technique can be classified into two categories based on their attack principles [4].”
BackgroundCollusion Resistant Multi-Matrix Masking for Privacy-Preserving Data Collection
Wu, S. S., Chen, S., Bhattacharjee, A., et al. (2017) · IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) · doi:10.1109/bigdatasecurity.2017.10 · Cited by 9
“There are several other methods to collect data anonymously without revealing the providers’ identity, including various cryptographic solutions [26], [27], [28] and anonymous communications [29], [30], [31].”
MethodChiang, F., & Gairola, D. (2017) · Journal of Data and Information Quality · doi:10.1145/3190577 · Cited by 9
“Incorporating data privacy alongside data management tasks has been wellstudied, including areas such as privacy preserving data publishing [27, 35, 39], data mining [3], schema and information integration [20, 44], record linkage [30], and data quality assessment [5].”
BackgroundModel Checking Differentially Private Properties
Liu, D., Wang, B.-Y., & Zhang, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02768-1_21 · Cited by 9
Blockchain-Enabled Peer-to-Peer Data Trading Mechanism
Sabounchi, M., Wei, J., & Roche, R. (2018) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/cybermatics_2018.2018.00241 · Cited by 9
“Furthermore, amongst different factors for designing P2P data trading mechanisms, two of the major ones are (1) the level of privacy concerns of the data owners [12], [13] and (2) the sufficient controls of the individual engaged parties [14].”
BackgroundRDF Graph Anonymization Robust to Data Linkage
Delanaux, R., Bonifati, A., Rousset, M.-C., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34223-4_31 · Cited by 9
“Whereas this is highly desirable in many applications, it becomes not sufficient in privacy-preserving data publishing (PPDP) [9] scenarios where the privacy of individuals need to be protected while at the same time ensuring that the published data can be utilized in practice.”
BackgroundOptimality and Complexity of Inference-Proof Data Filtering and CQE
Biskup, J., Bonatti, P. A., Galdi, C., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11212-1_10 · Cited by 9
“Many works focussed on databases [1–4]; later studies have been extended to knowledge bases, where inferences can naturally be automated using standard semantic web technology [5, 6].”
Background(l1, ..., lq)-diversity for Anonymizing Sensitive Quasi-Identifiers
Sei, Y., Takenouchi, T., & Ohsuga, A. (2015) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications / BigDataSE / ISPA (Trustcom/BigDataSE/ISPA) · doi:10.1109/trustcom.2015.424 · Cited by 9
“On the other hand, Table II does not satisfy [[2, 1]]-diversity, [[2, 2]]-diversity, or [[2, 3]]-diversity.”
BackgroundInternational Journal of Ethics in Engineering & Management Education
vision-i, P. (2014) · ADVANCED SCIENCES INDEX · Cited by 9
Fuzzy Prediction Model in Privacy Protection: Takagi–Sugeno Rules Model via Differential Privacy
Zhang, G., Zhu, X., Yin, L., Pedrycz, W., & Li, Z. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3380596 · Cited by 8
An overview of proposals towards the privacy-preserving publication of trajectory data
Miranda-Pascual, À., Guerra-Balboa, P., Parra‐Arnau, J., Forné, J., & Strufe, T. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00894-0 · Cited by 8
Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays
Till, T., Scherkl, M., Stranger, N., Singer, G., Hankel, S., Flucher, C., Hržić, F., Štajduhar, I., & Tschauner, S. (2025) · European Radiology · doi:10.1007/s00330-025-11669-z · Cited by 10
“The lack of a publicly accessible reference test set or a test set protected by a certi fi cation body stands in the way of an objective and comparable measurement of performance characteristics among different AI solutions [20].”
Trust and Risk-Based Access Control for Privacy Preserving Threat Detection Systems
Metoui, N., Michele Bezzi, A., & Armando, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48057-2_20 · Cited by 8
“3 Other privacy metrics exist (for example, `-diversity, and t-closeness, see [13] ), but k-anonymity is still a de-facto standard in real applications”
BackgroundDifferentially private histogram publishing through Fractal dimension for dynamic datasets
Yan, F., Zhang, X., Li, C., et al. (2018) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2018.8397954 · Cited by 8
“Even though we can apply several simple anonymization techniques [1-4], sensitive personal information still has a high probability to being disclosed [5].”
Privacy-Preserving Method for Trajectory Data Publication Based on Local Preferential Anonymity
Zhang, X., Luo, Y., Yu, Q., et al. (2023) · Information · doi:10.3390/info14030157 · Cited by 8
TLEF: Two-Layer Evolutionary Framework for t-Closeness Anonymization
You, M., Ge, Y.-F., Wang, K., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7254-8_18 · Cited by 8
Self-citationBalancing value and risk in information sharing through obfuscation
Chakraborty, S., Raghavan, K. R., Srivastava, M. B., et al. (2012) · Cited by 8
Secure Scalar Product for Big-Data in MapReduce
Liu, F., Ng, W. K., & Zhang, W. (2015) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice.2015.9 · Cited by 8
“In recent years, k-anonymity [11] as well as differential privacy [25], [18] are also proposed and have been adopted in many privacypreserving data mining techniques, such as [1] and [17].”
Parameterized Inapproximability of Degree Anonymization
Bazgan, C., & Nichterlein, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13524-3_7 · Cited by 8
“On the positive side, we classify the general decision version as fixedparameter tractable with respect to the combined parameter solution size s and maximum degree.”
MethodDataless Data Mining: Association Rules-Based Distributed Privacy-Preserving Data Mining
Ashok, V. G., Navuluri, K., Alhafdhi, A., et al. (2015) · International Conference on Information Technology: New Generations · doi:10.1109/itng.2015.102 · Cited by 8
“has been addressed by several researchers in the past [2][15], [19], [20].”
BackgroundA Coalitional Game Theoretic Mechanism for Privacy Preserving Publishing Based on k-Anonymity
Chakravarthy, S. L., Kumari, V. V., & Sarojini, C. (2012) · Procedia Technology · doi:10.1016/j.protcy.2012.10.108 · Cited by 8
Protecting Data Privacy from Being Inferred from High Dimensional Correlated Data
Ba, H., Gao, X., Zhang, X., et al. (2014) · IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT) · doi:10.1109/wi-iat.2014.139 · Cited by 8
“Generally in private data publishing [4], sensitive data features as long as other ordinary data features are public known.”
BackgroundPrivacy-preserving traffic padding in web-based applications
Liu, W. M., Wang, L., Cheng, P., et al. (2011) · ACM Workshop on Privacy in the Electronic Society (WPES) · doi:10.1145/2046556.2046573 · Cited by 8
“In this paper, we notice a striking similarity between the above privacy preserving traffic padding (PPTP) issue and another well studied problem, namely, privacy-preserving data publishing (PPDP) (as surveyed in [4]).”
BackgroundPrivacy-Preserving Health Data Collection for Preschool Children
Guan, S., Zhang, Y., & Ji, Y. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/501607 · Cited by 8
Semantic Security: Privacy Definitions Revisited
Liu, J., Xiong, L., & Luo, J. (2013) · Cited by 8
SvdNPD: a negative data publication method based on the sensitive value distribution
Wu, L., Luo, W., & Zhao, D. (2015) · doi:10.1109/aisw.2015.7469246 · Cited by 8
“Nevertheless, they are vulnerable to some attacks [2].”
BackgroundPoshida, a protocol for private information retrieval
Ullah, M., Khan, R., & Islam, M. A. (2016) · International Conference on Innovative Computing Technology · doi:10.1109/intech.2016.7845060 · Cited by 8
“)[5, 22] for business purposes or finding information about product of competitor companies.”
BackgroundA framework for utility enhanced incomplete microdata anonymization
Gong, Q., Yang, M., Chen, Z., et al. (2017) · Cluster Computing · doi:10.1007/s10586-017-0795-6 · Cited by 8
“At present, most existing anonymization algorithms [1,5, 24,31] first partition the original dataset into k-group, (also called k-anonymous groups), where each group contains at”
BackgroundMethodInfluentialk-Presence-Secrecy: Practical Privacy Model as Extension of k-Anonymity
Yamaoka, Y., & Itoh, K. (2017) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016dap0015 · Cited by 8
“However, δ -presence is not practical [1] because it requires the PPDP executor to have almost all the adversaries’ background knowledge.”
BackgroundMethodInfluentialYousra, S. A., & Mazleena, S. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1003/1/012030 · Cited by 8
Mohan, T., & Praveen, K. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9939-8_19 · Cited by 8
Privacy Preserving Sensitive Data Publishing using (k,n,m) Anonymity Approach
Victor, N., & Lopez, D. (2020) · Journal of Communications Software and Systems · doi:10.24138/jcomss.v16i1.825 · Cited by 8
Knowing Your Population: Privacy-Sensitive Mining of Massive Data
Sanches, P., Svee, E.-O., Bylund, M., et al. (2016) · Cited by 8
Mixing Transactions with Arbitrary Values on Blockchains
Ni, W., Cheng, P., & Chen, L. (2022) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde53745.2022.00240 · Cited by 8
“Inspired by the idea of confi-dence bounding [13], [14], we first define a novel anonymity concept, c -decomposition, to measure the anonymity of a decomposition solution.”
BackgroundMethodK-Anonymity for Privacy Preserving Crime Data Publishing in Resource Constrained Environments
Burke, M. J., & Kayem, A. V. D. M. (2014) · International Conference on Advanced Information Networking and Applications Workshops (WAINA) · doi:10.1109/waina.2014.131 · Cited by 8
“As mentioned before, existing data anonymity algorithms are better suited to handling numeric as opposed to categorical values [14], [25], [26].”
BackgroundLabel-bag based Graph Anonymization via Edge Addition
Li, C., Amagasa, T., Kitagawa, H., et al. (2008) · doi:10.1145/2641483.2641516 · Cited by 8
“To protect privacy information, many researchers have proposed techniques for privacy preservation [9].”
BackgroundPSST… privacy, safety, security, and trust in health information websites
Samuel, H., & Zai͏̈ane, O. R. (2012) · doi:10.1109/bhi.2012.6211650 · Cited by 8
“Considering the case of the AOL search logs incident, user data from other sites can be used to reidentify other anonymized records via various linkage attack models[8].”
BackgroundMethodk-anonymity based framework for privacy preserving data collection in wireless sensor networks
Bahşi, H., & Levi, A. W. (2010) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-0907-120 · Cited by 8
“Key Words: Anonymity; Wireless Sensor Networks; Data Privacy.”
BackgroundConstrained Anonymization of Production Data: A Constraint Satisfaction Problem Approach
Yahalom, R., Shmueli, E., & Zrihen, T. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15546-8_4 · Cited by 8
Choi, H. S., Chakraborty, S., & Srivastava, M. B. (2012) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2012.120 · Cited by 8
“This is because even simple personal information can be easily correlated with privacy-violating facts [23].”
BackgroundPrivacy-preserving publication of diagnosis codes for effective biomedical analysis
Loukides, G., Gkoulalas-Divanis, A., & Malin, B. (2010) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2010.5687720 · Cited by 8
“There are several privacy principles and algorithms for anonymizing relational data, such as patient demographics (see [ 14 ] for a survey).”
BackgroundPrivacy-preserving data warehousing
Fabian, B., & Göthling, T. (2015) · International Journal of Business Intelligence and Data Mining · doi:10.1504/ijbidm.2015.072210 · Cited by 8
Privacy and Utility Effects of k-anonymity on Association Rule Hiding
Tsai, Y., Wang, S.-L., Song, C.-Y., et al. (2016) · doi:10.1145/2955129.2955169 · Cited by 8
“Many techniques have been proposed to achieve privacy preservation, such as association rule hiding for protecting sensitive rules [1][4][6].”
BackgroundA New Approach to Utility-Based Privacy Preserving in Data Publishing
Vural, Y., & Aydos, M. (2017) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2017.27 · Cited by 8
An Information-Theoretic Approach to Time-Series Data Privacy
Amar, Y., Haddadi, H., & Mortier, R. (2018) · doi:10.1145/3195258.3195261 · Cited by 8
“Recent, comprehensive survey papers describe an extensive range of metrics for a vast array of different purposes [10] and organise these into taxonomies [22].”
BackgroundSynthesizing Differentially Private Datasets using Random Mixing
Lee, K., Kim, H., Lee, K., Suh, C., & Ramchandran, K. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849381 · Cited by 8
“In order to protect data, one may first synthesize a private version of the original training dataset and then train a model solely with the private one [2]–[4].”
BackgroundAggregation-based location privacy: An information theoretic approach
Zhang, W., Jiang, B., Li, M., Tandon, R., Liu, Q., & Li, H. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101953 · Cited by 8
Métayer, D. L., & De, S. J. (2016) · HAL · doi:10.2200/s00724ed1v01y201607spt017 · Cited by 8
COVID-19 has illuminated the need for clearer AI-based risk management strategies
Swanson, T., Zelner, J., & Guikema, S. D. (2022) · Journal of Risk Research · doi:10.1080/13669877.2022.2077411 · Cited by 8
Privacy Preserving Data Anonymization of Spontaneous ADE Reporting System Dataset
Lin, W.-Y., Yang, D.-C., & Wang, J.-T. (2015) · doi:10.1145/2811163.2811176 · Cited by 8
Two-phase entropy based approach to big data anonymization
Ranjan, A., & Ranjan, P. (2016) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2016.7813693 · Cited by 8
“Different generalization schemes as discussed in [5] are as follows;”
BackgroundSurvey on Big data Analytics for digital world
Gaikwad, S., Nale, P., & Bachate, R. P. (2016) · doi:10.1109/icaecct.2016.7942579 · Cited by 8
Técnicas de combinação para agrupamento centralizado e distribuído de dados
Naldi, M. C. (2011) · doi:10.11606/t.55.2011.tde-16032011-113154 · Cited by 8
Abstracting Anonymization Techniques: A Prerequisite for Selecting a Generalization Algorithm
Fredj, F. B., Lammari, N., & Comyn-Wattiau, I. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.08.120 · Cited by 8
“Moreover, some of them are bottom up processes 14 i.e. small groups of tuples are constituted and then iteratively merged until each group contains at least k rows (k-anonymity satisfaction) 20 .”
BackgroundMethodTRADE: TRusted Anonymous Data Exchange: Threat Sharing Using Blockchain TechnologyPreprint
Allouche, Y., Tapas, N., Longo, F., Shabtai, A., & Wolfsthal, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.13158 · Cited by 8
“[21] Privacy preserving CTI sharing framework Centralized X – – – –”
Li, X., Wang, Y., Song, J., Liu, Y., Zhang, X., Zhou, K., & Li, C. (2020) · World Wide Web · doi:10.1007/s11280-019-00769-8 · Cited by 7
“To address this problem, we can perturb the information of this tree to achieve preserving privacy [7] before publishing, which is called preserving privacy data publishing [8, 9].”
BackgroundConstrained obfuscation of relational databases
Shmueli, E., Zrihen, T., Yahalom, R., et al. (2014) · Information Sciences · doi:10.1016/j.ins.2014.07.009 · Cited by 7
From Theory to Comprehension: A Comparative Study of Differential Privacy and k-Anonymity
von Voigt, S. N., Mehner, L., & Tschorsch, F. (2024) · doi:10.1145/3626232.3653261 · Cited by 7
Maximizing data quality while ensuring data protection in service-based data pipelines
Polimeno, A., Braghin, C., Anisetti, M., et al. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01118-5 · Cited by 7
Distributed Bandit-Based Cooperative Coevolution for Large-Scale Multi-Objective Data Publishing
Ge, Y.-F., Wang, H., Bertino, E., Cao, J., Zhang, Y., & Zheng, Z. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3609875 · Cited by 7
“Fung et al. [22] examined attack models (record linkage, attributed linkage, and table linkage) and privacy models such as k - anonymity[24], l -diversity[25],and t -closeness[26].”
Privacy-Preserving Data Collection with Self-Awareness Protection
Wong, K., & Kim, M. H. (2014) · Lecture notes in electrical engineering · doi:10.1007/978-94-017-8798-7_44 · Cited by 7
On Data Publishing with Clustering Preservation
Vlachos, M., Schneider, J., & Vassiliadis, V. G. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2700403 · Cited by 7
Liu, W. M., & Wang, L. (2012) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/2133601.2133626 · Cited by 7
“Privacy Streamliner: A Two-Stage Approach to Improving Algorithm Ef.ciency Wen Ming Liu and Lingyu Wang Concordia Institute for Information Systems Engineering, Concordia University Montreal, QC H3G 1M8, Canada {l_wenmin,wang}@ciise.concordia.ca ABSTRACT In releasing data with sensitive…”
MethodReconstruction Privacy: Enabling Statistical Learning
Wang, K., Han, C., Fu, A. W.-C., et al. (2015) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.5441/002/edbt.2015.41 · Cited by 7
“See surveys [1][2][3] for details.”
Self-citationPrivacy-Aware Data Sharing in a Tree-Based Categorical Clustering Algorithm
Sheikhalishahi, M., Mejri, M., Tawbi, N., & Martinelli, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-51966-1_11 · Cited by 7
“In what follows, we first prove that if all agents fix the stop condition (the minimum number of elements in leaves) of CCTree equal to μ, then publishing the result as a CCTree term satisfies k-anonymous dataset (k is dependent to μ) [4].”
BackgroundResearch on K anonymity algorithm based on association analysis of data utility
Gao, Y., Luo, T., Li, J., et al. (2017) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac.2017.8054050 · Cited by 7
“Anonymous generalization algorithm can be divided into global algorithm and local algorithm two kinds [3, 4].”
BackgroundHide and Mine in Strings: Hardness, Algorithms, and Experiments
Bernardini, G., Conte, A., Gourdel, G., et al. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3158063 · Cited by 7
Clinical Data Integration Strategies for Multicenter Studies
Almeida, J. R., Pazos, A., & Oliveira, J. L. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-36007-7_13 · Cited by 7
Wabo, G. K., Prasser, F., Gierend, K., Siegel, F., & Ganslandt, T. (2023) · JMIR Research Protocols · doi:10.2196/46471 · Cited by 6
Differentially private distributed online mirror descent algorithm
Yuan, M., Lei, J., & Hong, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126531 · Cited by 8
De-identification of Textual Data Using Immune System for Privacy Preserving in Big Data
Rahmani, A., Amine, A., & Hamou, R. M. (2015) · doi:10.1109/cict.2015.146 · Cited by 7
Efficient discovery of de-identification policy options through a risk-utility frontier
Xia, W., Heatherly, R. D., Ding, X., et al. (2013) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/2435349.2435357 · Cited by 7
“This topic is a portion of the broader issue of privacy-preserving data publishing, for which we direct readers to [15] for an excellent survey.”
BackgroundAvoiding Disclosure of Individually Identifiable Health Information
Prada, S. I., González-Martínez, C., Borton, J., et al. (2011) · SAGE Open · doi:10.1177/2158244011431279 · Cited by 7
“Other recent technical reviews are those by B. Chen, Kifer, LeFevre, and Machanavajjhala (2009) and Fung, Wang, Chen, and Yu (2010) on the latest developments in the field of Privacy-preserving data publishing, and by Fayyoumi and Oommen (2010) on statistical disclosure control and microaggregation…”
A Parallel Method for Scalable Anonymization of Transaction Data
Memon, N., Loukides, G., & Shao, J. (2015) · International Symposium on Parallel and Distributed Computing · doi:10.1109/ispdc.2015.34 · Cited by 7
“There have been different approaches to data anonymization, and we refer the reader to [9]–[11] for surveys.”
BackgroundPrivacy Preserving Data Mining Using Association Rule With Condensation Approach
Borhade, S. S., & Shinde, B. B. (2014) · Cited by 7
A survey on Privacy Preserving Data Mining
Natarajan, R., Sugumar, R., Mahendran, M., & Anbazhagan, K. (2012) · Cited by 7
Customized privacy preserving for classification based applications
He, Z., Cai, Z., & Li, Y. (2016) · doi:10.1145/2940343.2940345 · Cited by 7
“Many privacy preserving data mining techniques distort or hide data before release data [10] [18].”
BackgroundSmartphone Based Real-Time Health Monitoring and Intervention
Aranki, D., Kurillo, G., & Bajcsy, R. K. (2017) · Scalable computing and communications · doi:10.1007/978-3-319-58280-1_18 · Cited by 7
Preservation of temporal privacy in body sensor networks
Diyanat, A., Khonsari, A., & Shafiei, H. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.07.015 · Cited by 7
AN EFFECTIVE CLUSTERING APPROACH TO WEB QUERY LOG ANONYMIZATION
Fard, A. M., & Wang, K. (2010) · International Conference on Security and Cryptography · doi:10.5220/0002924901090119 · Cited by 7
Self-citationDistributed Learning With Dependent Samples
Sun, Z., & Lin, S.-B. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3175761 · Cited by 8
“com) clinical decision-making, in the premise of preserving the privacy of their own medical records [23].”
BackgroundSeeman, J., Slavković, A., & Reimherr, M. L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57521-2_23 · Cited by 7
Remodeling: improved privacy preserving data mining (PPDM)
Shastri, M. D., & Pandit, A. A. (2020) · International Journal of Information Technology · doi:10.1007/s41870-020-00531-8 · Cited by 7
“Privacy Preserving Data Publishing (PPDP) mostly focuses on publishing the data [11].”
BackgroundMethodA Movie Recommendation System Based on Differential Privacy Protection
Li, M., Zeng, Y., Guo, Y., et al. (2020) · Security and Communication Networks · doi:10.1155/2020/6611463 · Cited by 7
Wang, J.-T., & Lin, W.-Y. (2021) · JMIR Medical Informatics · doi:10.2196/28752 · Cited by 7
Tian, H., Zheng, X., Zhang, X., & Zeng, D. D. (2021) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2021.101105 · Cited by 7
PRIVEE: A Visual Analytic Workflow for Proactive Privacy Risk Inspection of Open Data
Bhattacharjee, K., Islam, A., Vaidya, J. S., & Dasgupta, A. (2022) · IEEE Symposium on Visualization for Cyber Security (VIZSEC) · doi:10.1109/vizsec56996.2022.9941431 · Cited by 7
“A suite of anonymization methods [29] exists to address the problem of linking among public and private datasets, for example, between Census data and hospital records.”
PriGen: A Generic Framework to Preserve Privacy of Healthcare Data in the Cloud
Rahman, F., Ahamed, S. I., Yang, J., & Wang, Q. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39470-6_10 · Cited by 6
Location l-Diversity against Multifarious Inference Attacks
Miyakawa, S., Saji, N., & Mori, T. (2012) · IEEE/IPSJ International Symposium on Applications and the Internet (SAINT) · doi:10.1109/saint.2012.11 · Cited by 7
Ontology-Enhanced Interactive Anonymization in Domain-Driven Data Mining Outsourcing
Loh, B. C. S., & Then, P. (2010) · doi:10.1109/isdpe.2010.7 · Cited by 7
“Anonymization allows concealment of patient identities or sensitive data, assuming that this information is required for data analysis [9].”
BackgroundExploring new privacy approaches in a scalable classification framework
Saravanan, M., Thoufeeq, A. M., Akshaya, S., & Manchari, V. L. J. (2014) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2014.7058075 · Cited by 7
“These techniques involve data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques [4].”
Method
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
