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 301–400 of 1,001
TLKC-Privacy Model for Process Mining
Rafiei, M., Wagner, M., & van der Aalst, W. M. P. (2020) · Lecture notes in business information processing · doi:10.1007/978-3-030-50316-1_24 · Cited by 32
“Considering the typical scenario of data collection and data publishing [7], we assume the trusted model , where the data holder (here, a hospital) is trustworthy.”
BackgroundKanwal, T., Anjum, A., Malik, S. U. R., et al. (2021) · Computer Standards & Interfaces · doi:10.1016/j.csi.2021.103522 · Cited by 32
Privacy Technology to Support Data Sharing for Comparative Effectiveness Research
Jiang, X., Sarwate, A. D., & Lucila Ohno-machado, A. (2013) · Medical Care · doi:10.1097/mlr.0b013e31829b1d10 · Cited by 30
“50,51,80 One line of approach, including k -anonymity, as introduced earlier, manipulates the data to merge unique individuals, sanitizing tables through table “anonymization” 33,81,82 (ie, generalization or suppression) before publication.”
BackgroundYin, L., Wang, Q., Shaw, S., Fang, Z., Hu, J., Ye, T., & Wang, W. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140589 · Cited by 36
Privacy-preserving topic model for tagging recommender systems
Zhu, T., Li, G., Zhou, W., Xiong, P., & Cao, Y. Y. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-015-0832-9 · Cited by 31
“An adversarywith background informationmay re-identify a particular user in a tagging dataset and obtain the user’s historical tagging records [11].”
BackgroundUtility-preserving privacy protection of nominal data sets via semantic rank swapping
Rodríguez-García, M., Batet, M., & Sánchez, D. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.02.008 · Cited by 31
Zhang, C. B. C., Hu, X., Xie, Y., Gong, M., & Yu, B. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00112 · Cited by 32
Domadiya, N. H., & Rao, U. P. (2020) · Computing · doi:10.1007/s00607-020-00847-0 · Cited by 31
Srijayanthi, S., & Sethukarasi, T. (2022) · Computers & Security · doi:10.1016/j.cose.2022.103027 · Cited by 31
Abdelhameed, S. A., Moussa, S. M., & Khalifa, M. E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.08.017 · Cited by 31
Privacy-enhancing ETL-processes for biomedical data
Praßer, F., Spengler, H., Bild, R., et al. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.03.006 · Cited by 31
“Re-iden-ti fi cation can be understood as a linkage process [24]: the uniqueness of (combinations of) attributes is exploited to link records of datasets with additional data or background knowledge of the adversary.”
A general framework for privacy preserving data publishing
Sattar, A. H. M. S., Li, J., Ding, X., Liu, J., & Vincent, M. W. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.09.022 · Cited by 30
“For details readers are referred to [20, 21].”
BackgroundPrivacy preserving sub-feature selection based on fuzzy probabilities
Bhuyan, H. K., & Kamila, N. K. (2014) · Cluster Computing · doi:10.1007/s10586-014-0393-9 · Cited by 30
“The data privacy or privacy preservation of data has been developed by using standard and secure multiparty computation [15], game theory [16], K- anonymity and l-diversity approach in social network [17], privacy preserving data publishing [18], horizontal partitioned data [19] etc.”
MethodHigh utility K-anonymization for social network publishing
Wang, Y., Xie, L., Zheng, B., & Lee, K. C. K. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0674-2 · Cited by 30
Cryptography for Security and Privacy in Cloud Computing
Raß, S., & Slamanig, D. (2013) · Cited by 30
Collaborative analytics for data silos
Kim, J., Ha, H., Chun, B.-G., Yoon, S., & Cha, S. K. (2016) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2016.7498286 · Cited by 32
“The information about the degree of trustfulness is often not provided [4].”
BackgroundMethodInfluentialMining Periodic High Utility Sequential Patterns
Dinh, D.-T., Huynh, V., & Le, B. H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54472-4_51 · Cited by 30
Population mobility modelling for mobility data simulation
Smolak, K., Rohm, W., Knop, K., & Siła-Nowicka, K. (2020) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2020.101526 · Cited by 30
A transversal hypergraph approach for the frequent itemset hiding problem
Stavropoulos, E. C., Verykios, V. S., & Kagklis, V. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-015-0862-3 · Cited by 30
“It is universally accepted that simplistic approaches to the preservation of privacy, like the removal of identifiers and sensitive attributes, are inadequate in accomplishing the desired effects, since there is an ample number of counter measures like the use of quasi-identifiers and inference-based approaches that u…”
BackgroundPrivacy modeling for video data publication
Saini, M. K., Atrey, P. K., Mehrotra, S., Emmanuel, S., & Kankanhalli, M. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5583334 · Cited by 29
“Inference channels are well studied in statistical data publication [1].”
BackgroundImproved Upper and Lower Bound Heuristics for Degree Anonymization in Social Networks
Hartung, S., Hoffmann, C., & Nichterlein, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07959-2_32 · Cited by 29
“In a landmark paper [12] initiating a lot of follow-up work [4, 11, 13], 1 Liu and Terzi transferred the so-called k-anonymity concept known for tabular data in databases [9, 14, 15, 16] to social networks modeled as undirected graphs.”
BackgroundKanwal, T., Shaukat, S. A. A., Anjum, A., Malik, S. U. R., Choo, K. R., Khan, A., Ahmad, N., Ahmad, M., & Khan, S. U. (2019) · Information Sciences · doi:10.1016/j.ins.2019.03.004 · Cited by 29
Sensitive Label Privacy Preservation with Anatomization for Data Publishing
Yao, L., Chen, Z., Wang, X., Liu, D., & Wu, G. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2919833 · Cited by 29
“Perturbation distorts the data by adding noise, swapping values, or generating synthetic data [4].”
BackgroundInfluentialPrivacy preserving data sharing and analysis for edge-based architectures
Sheikhalishahi, M., Saracino, A., Martinelli, F., & Marra, A. L. (2021) · International Journal of Information Security · doi:10.1007/s10207-021-00542-x · Cited by 29
A Universal Toolkit for Cryptographically Secure Privacy-Preserving Data Mining
Bogdanov, D., Jagomägis, R., & Laur, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30428-6_9 · Cited by 29
De-identifying a public use microdata file from the Canadian national discharge abstract database
Emam, K. E., Paton, D., Dankar, F. K., & Koru, G. (2011) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-11-53 · Cited by 29
“The formulas for IG and PL can be seen in [45].”
BackgroundMethodHua, J., Tang, A., Fang, Y., Shen, Z., & Zhong, S. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2532839 · Cited by 30
“As a result, the users of the published data usually have a strong demand to verify the real utility of the anonymized data [13].”
BackgroundK-Anonymity Algorithm Based on Improved Clustering
Zheng, W., Wang, Z.-Y., Lv, T., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05054-2_36 · Cited by 29
Blind Analysis as a Correction for Confirmatory Bias in Physics and in Psychology
MacCoun, R. J., & Perlmutter, S. (2017) · Wiley eBooks · doi:10.1002/9781119095910.ch15 · Cited by 34
PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation
Liao, X., Liu, W., Zheng, X., et al. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25566 · Cited by 28
Synthetic data: revisiting the privacy-utility trade-off
Sarmin, F. J., Sarkar, A. R., Wang, Y., & Mohammed, N. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01072-6 · Cited by 38
A refined complexity analysis of degree anonymization in graphs
Hartung, S., Nichterlein, A., Niedermeier, R., et al. (2014) · Information and Computation · doi:10.1016/j.ic.2014.12.017 · Cited by 28
“Herein, they transferred the k-anonymity concept known for tabular data in databases [9] to graphs.”
BackgroundTowards semantically secure outsourcing of association rule mining on categorical data
Lai, J., Li, Y., Deng, R. H., Weng, J., Guan, C., & Yan, Q. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.040 · Cited by 28
Secure and Private Management of Healthcare Databases for Data Mining
Mohammed, N., Barouti, S., Alhadidi, D., et al. (2015) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms.2015.54 · Cited by 28
Self-citationTwo Privacy-Preserving Approaches for Publishing Transactional Data Streams
Wang, J., Deng, C., & Li, X. (2018) · IEEE Access · doi:10.1109/access.2018.2814622 · Cited by 28
“posed for different data publishing scenarios [1]–[7].”
Valuing Personal Data with Privacy Consideration
Li, X., Liu, X., & Motiwalla, L. (2020) · Decision Sciences · doi:10.1111/deci.12442 · Cited by 28
Privacy Preservation over Big Data in Cloud Systems
Zhang, X., Liu, C., Nepal, ., Yang, C., & Chen, J. (2013) · Springer eBooks · doi:10.1007/978-3-642-38586-5_8 · Cited by 28
Time-Series Pattern Based Effective Noise Generation for Privacy Protection on Cloud
Zhang, G., Liu, X., & Yang, Y. J. (2014) · IEEE Transactions on Computers · doi:10.1109/tc.2014.2298013 · Cited by 26
Logical Foundations of Privacy-Preserving Publishing of Linked Data
Grau, B. C., & Kostylev, E. V. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10105 · Cited by 25
“For details, we refer the reader to the excellent survey in (Fung et al. 2010) and our Related Techniques section.”
MethodZhang, X., Leckie, C. A., Dou, W., Chen, J., Ramamohanarao, K., & Salcic, Z. A. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983841 · Cited by 27
“Data anonymization has been extensively studied and widely adopted for data privacy preservation in non-interactive data sharing and dissemination scenarios [11].”
BackgroundInfluentialPLDP-TD: Personalized-location differentially private data analysis on trajectory databases
Deldar, F., & Abadi, M. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.06.005 · Cited by 28
Bhattacharjee, K., Chen, M., & Dasgupta, A. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14032 · Cited by 30
A top-down approach for approximate data anonymisation
Li, J., Yang, J., Zhao, Y., & Liu, B. (2012) · Enterprise Information Systems · doi:10.1080/17517575.2012.688223 · Cited by 27
“But both of them charge the information loss for generalising a value in a record independently of other records (Benjamin Fung et al. 2010).”
BackgroundMethodResultInfluentialA differentially private algorithm for location data release
Xiong, P., Zhu, T., Niu, W., et al. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-015-0856-1 · Cited by 27
“Hence, traditional privacy approaches hardly provide sufficient protection to users, due to the difficulty in modeling background knowledge [12].”
BackgroundAn Ecosystemic and Socio-Technical View on Software Maintenance and Evolution
Mens, T. (2016) · IEEE International Conference on Software Maintenance and Evolution (ICSME) · doi:10.1109/icsme.2016.19 · Cited by 27
“presented a survey of research results and future directions in privacy-preserving data publishing [70].”
BackgroundPolicy-based usage control for a trustworthy data sharing platform in smart cities
Cao, Q. H., Madhusudan, G., Farahbakhsh, R., et al. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.05.039 · Cited by 27
“that she does not breach the privacy of the data subjects still retains sufficient utility for the data recipients [23].”
BackgroundTrust management of services ( TMoS ): Investigating the current mechanisms
Hayyolalam, V., Pourghebleh, B., & Kazem, A. A. P. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4063 · Cited by 27
A review of preserving privacy in data collected from buildings with differential privacy
Janghyun, K., Barry, H., Tianzhen, H., & P, M. A. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.104724 · Cited by 27
Decomposition+: Improving ℓ-Diversity for Multiple Sensitive Attributes
Das, D., & Bhattacharyya, D. K. (2012) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-27308-7_44 · Cited by 27
Distance-Based k^m-Anonymization of Trajectory Data
Poulis, G., Skiadopoulos, S., Loukides, G., et al. (2013) · Proceedings/IEEE International Conference on Mobile Data Management · doi:10.1109/mdm.2013.66 · Cited by 27
“Moreover, applying perturbation to datasets, creates data that are not truthful and cannot be used in several applications [9].”
MethodPrivacy preserving event sequence data visualization using a Sankey diagram-like representation
Chou, J., Wang, Y., & Ma, K. (2016) · SIGGRAPH Asia Symposium on Visualization · doi:10.1145/3002151.3002153 · Cited by 26
“In the data analytics field, an abundance of research efforts [Fung et al. 2010; Gkoulalas-Divanis et al. 2014] have been dedicated to- wards the preservation of privacy before the publication of data. kanonymity [Sweeney 2002] is one of the early models proposed for such purpose.”
BackgroundA systematic review of privacy techniques in recommendation systems
Ogunseyi, T. B., Avoussoukpo, C. B., & Jiang, Y. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00710-1 · Cited by 27
“Corporations today collect huge digital personal and nonpersonal information from internet users, which has created tremendous benefits for knowledge-based decision-making [1, 2].”
BackgroundTask Oriented Privacy Preserving Data Publishing Using Feature Selection
Jafer, Y., Matwin, S., & Sokolova, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06483-3_13 · Cited by 26
Notice of Removal February 26, 2026: FAST: Fast Anonymization of Big Data Streams
Mohammadian, E., Noferesti, M., & Jalili, R. (2014) · International Conference on Big Data Science and Computing (BigDataScience) · doi:10.1145/2640087.2644149 · Cited by 26
Information preservation in statistical privacy and bayesian estimation of unattributed histograms
Lin, B.-R., & Kifer, D. (2013) · Proceedings - ACM-SIGMOD International Conference on Management of Data · doi:10.1145/2463676.2463721 · Cited by 26
“As a result, utility was often measured as some notion of “closeness” between the original data and sanitized data (see [13, 19, 2] for references to numerous utility metrics of this type) or“closeness”of a statistical analysis conducted on both datasets (e.”
TAPAS: Trustworthy privacy-aware participatory sensing
Kazemi, L., & Shahabi, C. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0573-y · Cited by 26
“m-Anonymity is also a well-known technique in privacy-preserving data publishing [10], where a data publisher releases the data collected from the data owners to a data miner who will then conduct data mining on the published data, while the privacy of the data owners should be preserved.”
BackgroundPrivacy Preserving Unstructured Big Data Analytics: Issues and Challenges
Mehta, B. B., & Rao, U. P. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.02.020 · Cited by 26
Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives
Yan, K., Shen, W., Jin, Q., & Lu, H. (2019) · IEEE Access · doi:10.1109/access.2019.2894344 · Cited by 27
“Although there are similar works concerning privacy in sharing practices from the literature, e.g., [2, 8, 48, 59, 116], they focused on traditional privacy protection methods.”
BackgroundTwo privacy-preserving approaches for data publishing with identity reservation
Wang, J., Du, K., Luo, X., & Li, X. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1237-3 · Cited by 26
Scalable, High-Performance, and Generalized Subtree Data Anonymization Approach for Apache Spark
Bazai, S. U., Jang‐Jaccard, J., & Alavizadeh, H. (2021) · Electronics · doi:10.3390/electronics10050589 · Cited by 26
“Steps 4–7 are used to compute the privacy and utility scores which are denoted as Score ILPG (QID) as following, based on [41,42]. where IL ( QID ) contains the result of information loss for QID while PG ( QID ) contains the result of privacy gain for QID .”
MethodA Comparative Study of Privacy-Preserving Homomorphic Encryption Techniques in Cloud Computing
Joshi, B. K., Joshi, B., Mishra, A., et al. (2022) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.309936 · Cited by 26
Sharing Data and Models in Software Engineering
Menzies, T., Kocagüneli, E., Turhan, B., Minku, L. L., & Peters, F. (2014) · Cited by 29
Big Data and the Internet of Things
Shah, M. (2015) · Studies in big data · doi:10.1007/978-3-319-26989-4_9 · Cited by 26
“…data mining has developed into a subfield and considerable effort has gone into studying privacy challenges in data mining (Matwin 2013, Navarro-Arribas and Torra 2014), data publishing (Fung et al. 2010) and, to some extent, integration and interactions of sensors (Aggarwal and Abdelzaher 2011).”
BackgroundPrivacy-preserving publication of trajectories using microaggregation
Domingo‐Ferrer, J., Sramka, M., & Trujillo-Rasúa, R. (2010) · doi:10.1145/1868470.1868478 · Cited by 26
“Unfortunately, the traditional anonymization and sanitization methods for microdata [9] cannot be directly applied to spatio-temporal data without considerable expenses in computation time and information loss.”
BackgroundKrishnan, S., Wang, J., Franklin, M. J. M., Goldberg, K., & Kraska, T. (2016) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2882903.2915248 · Cited by 27
“There are a number of surveys describing deterministic techniques for privacy preserving analytics [4,13].”
BackgroundCooperative privacy game: a novel strategy for preserving privacy in data publishing
Kumari, V., & Chakravarthy, S. L. (2016) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-016-0069-y · Cited by 26
A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems
Sangogboye, F. C., Jia, R., Hong, T., Spanos, C. J., & Kjærgaard, M. B. (2018) · ACM Transactions on Sensor Networks · doi:10.1145/3275520 · Cited by 25
“This assumption is also referred to as the trusted model (Fung et al. 2010).”
BackgroundResultIntegrating Technical and Legal Concepts of Privacy
Sokolovska, A., & Kocarev, L. (2018) · IEEE Access · doi:10.1109/access.2018.2836184 · Cited by 24
“The second concern is related to the fact that a user does not have access to the overall distribution of sensitive values implying more protective decisions over her/his data and, thus, decreasing data utility [101].”
BackgroundOn providing location privacy for mobile sinks in wireless sensor networks
Ngai, E. C. ‐., & Rodhe, I. (2009) · ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) · doi:10.1145/1641804.1641825 · Cited by 25
“A survey on recent developments in privacy preserving transformations can be found in [25].”
BackgroundA hybrid approach to prevent composition attacks for independent data releases
Li, J., Baig, M. M., Sattar, A. H. M. S., Ding, X., Liu, J., & Vincent, M. W. (2016) · Information Sciences · doi:10.1016/j.ins.2016.05.009 · Cited by 25
Three Branches of Negative Representation of Information: A Survey
Luo, W., Liu, R., Jiang, H., Zhao, D., & Wu, L. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2829907 · Cited by 25
“However, these methods still have limitations as discussed in [114].”
MethodSensitive attribute privacy preservation of trajectory data publishing based on l-diversity
Yao, L., Chen, Z., Hu, H., Wu, G., & Wu, B. L. (2020) · Distributed and Parallel Databases · doi:10.1007/s10619-020-07318-7 · Cited by 25
Anonymizing data with quasi-sensitive attribute values
Pu, S., Xiong, L., & Fung, B. C. M. (2010) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1871437.1871628 · Cited by 25
“Privacy-preserving data publishing or data anonymization [2] has received considerable attention in recent years as a promising approach for sharing useful information while preserving data privacy.”
BackgroundMethodSelf-citationPrivacy preserving data anonymization of spontaneous ADE reporting system dataset
Lin, W.-Y., Yang, D.-C., & Wang, J.-T. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0293-4 · Cited by 25
“This procures the need of anonymizing the raw data before being published, namely privacy-preserving data publishing (PPDP) [4].”
BackgroundLogical Foundations of Linked Data Anonymisation
Grau, B. C., & Kostylev, E. V. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11355 · Cited by 25
“As a result, PPDP has become an increasingly important problem in recent years and several anonymisation techniques have been proposed in the context of relational databases (Fung et al., 2010).”
BackgroundDemystifying privacy in sensory data: A QoI based approach
Chakraborty, S., Choi, H. S., & Srivastava, M. B. (2011) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2011.5766914 · Cited by 24
“A detailed survey of other techniques can be found in [19].”
BackgroundAnjum, A., & Raschia, G. (2017) · Computers · doi:10.3390/computers6010001 · Cited by 24
“[7] provide a detailed survey on the approximation algorithms for k-anonymity.”
BackgroundJia, R., Sangogboye, F. C., Hong, T., Spanos, C. J., & Kjærgaard, M. B. (2017) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3137133.3137140 · Cited by 23
“This assumption is also referred to as the trusted model [13].”
BackgroundFast Privacy-Preserving Keyword Search on Encrypted Outsourced Data
Wodi, B. H., Leung, C. K.-S., Cuzzocrea, A., & Sourav, S. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9046058 · Cited by 24
“1) Privacy-Preserving Data Publishing ( PPDP ) : In a PPDP model [12], [13], data are published without disclosing identity of the data subjects.”
BackgroundHow the Accuracy and Confidence of Sensitivity Classification Affects Digital Sensitivity Review
McDonald, G., Macdonald, C., & Ounis, I. (2020) · ACM Transactions on Information Systems · doi:10.1145/3417334 · Cited by 24
“However, although there has been a substantial amount of research looking at identifying and masking personal information, e.g., Fung et al. [2010]; Sánchez and Batet [2016]; Sweeney [2002], it is only relatively recently that research has advanced in the field of sensitivity classification that we…”
BackgroundImplications of Data Anonymization on the Statistical Evidence of Disparity
Xu, H., & Zhang, N. (2022) · Management Science · doi:10.1287/mnsc.2021.4028 · Cited by 25
An adaptive access control scheme based on trust degrees for edge computing
Zheng, W., Chen, B., & He, D. (2022) · Computer Standards & Interfaces · doi:10.1016/j.csi.2022.103640 · Cited by 24
How Much Noise Suffices for Privacy of Multiagent Systems?
Zhang, W., Zuo, Z., Wang, Y., & Hu, G. (2022) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2022.3232050 · Cited by 24
“…[6], deterministic privacy-injection mechanism [7], [8], l -diversity/ k -anonymity [9], t -closeness [10], and privacy-preserving data [11], isomorphism transformation methods [12], etc. Recently, a continuous influx of research interests have…”
BackgroundData-Centric Foundation Models in Computational Healthcare: A Survey
Zhang, Y., Gao, J. G., Tan, Z., Zhou, L., Ding, K., Zhou, M., Zhang, S., & Wang, D. (2026) · ACM Computing Surveys · doi:10.1145/3800677 · Cited by 65
Improving accuracy of classification models induced from anonymized datasets
Last, M., Tassa, T., Zhmudyak, A., & Shmueli, E. (2013) · Information Sciences · doi:10.1016/j.ins.2013.07.034 · Cited by 24
“Their approach does not agree with the ‘‘non-expert data publisher’’ assumption [7] according to which many data owners do not have expertise in data mining and they are interested to publish their data only once (e.”
BackgroundInfluentialDistributing privacy policies over multimedia content across multiple online social networks
Patsakis, C., Zigomitros, A., Papageorgiou, A., & Galván, E. (2014) · Computer Networks · doi:10.1016/j.comnet.2014.08.023 · Cited by 24
“Therefore, special anonymization techniques, belonging to what is called privacy-preserving data publishing [11], are being applied to protect users [36,12].”
R-U policy frontiers for health data de-identification
Xia, W., Heatherly, R. D., Ding, X., Li, J., & Malin, B. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv004 · Cited by 23
“, age as [0–120]), in each level, the construction process splits each generalized interval into two equal size intervals.”
BackgroundUtility-guided Clustering-based Transaction Data Anonymization
Gkoulalas-Divanis, A., & Loukides, G. (2012) · ORCA Online Research @Cardiff (Cardiff University) · Cited by 24
A probabilistic approach to mitigate composition attacks on privacy in non-coordinated environments
Sattar, A. H. M. S., Li, J., Liu, J., Heatherly, R. D., & Malin, B. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.04.019 · Cited by 24
“While a substantial quantity of privacy-preserving data publication models and algorithms have been developed over the past decade [7], they do not appropriately address the non-coordinated composition problem.”
BackgroundInfluentialPrivacy in Sensor-Driven Human Data Collection: A Guide for PractitionersPreprint
Stopczynski, A., Pietri, R., Pentland, A., Lazer, D. M. J., & Lehmann, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.5299 · Cited by 38
“For a classification of different privacy implementation scenarios—such as multiple, sequential, continuous, or collaborative data publishing—see [113].”
BackgroundSliding windows over uncertain data streams
Dallachiesa, M., Jacques-Silva, G., Gedik, B., et al. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-014-0804-5 · Cited by 23
“In many of these application domains, the data sources available for processing can be considereduncertain, because of the imprecisions that arise from the inherent inaccuracy of sensor devices, or of external data manipulations like privacy-preserving data transformations [19].”
BackgroundModeling Privacy Aware Information Sharing Systems: A Formal and General Approach
Martinelli, F., Saracino, A., & Sheikhalishahi, M. (2016) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications / BigDataSE / ISPA (Trustcom/BigDataSE/ISPA) · doi:10.1109/trustcom.2016.0137 · Cited by 23
“[19], which presents a study to summarize and evaluate different approaches to privacy preserving data publishing (PPDP).”
BackgroundGao, J., Sun, L., & Cai, M. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.04.022 · Cited by 23
Emerging Privacy and Trust Issues for Autonomous Vehicle Systems
Nguyen, T.-H., Vu, T. G., Tran, H.-L., & Wong, K. (2022) · International Conference on Information Networking · doi:10.1109/icoin53446.2022.9687196 · Cited by 23
An Information-Driven Genetic Algorithm for Privacy-Preserving Data Publishing
Ge, Y.-F., Wang, H., Cao, J., & Zhang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20891-1_24 · Cited by 23
A taxonomy of data governance decision domains in data marketplaces
Abraham, R., Schneider, J., & Brocke, J. V. (2023) · Electronic Markets · doi:10.1007/s12525-023-00631-w · Cited by 23
“Examples include the utilization of anonymization techniques to hide identity data (Fung et al., 2010; Ha et al., 2019) and the application of homomorphic encryption to enable mathematical operations on encrypted data (Roman & Stefano, 2016).”
MethodPreservative License Plate De-identification for Privacy Protection
Du, L., & Ling, H. (2011) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2011.100 · Cited by 23
Utility-Aware Anonymization of Diagnosis Codes
Loukides, G., & Gkoulalas-Divanis, A. (2012) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/titb.2012.2212281 · Cited by 22
Using digital rights management for securing data in a medical research environment
Jafari, M., Safavi–Naini, R., Saunders, C., & Sheppard, N. P. (2010) · doi:10.1145/1866870.1866883 · Cited by 23
“Copyright 2010 ACM 978-1-4503-0091-9/10/10 ...$10.00. these patients and re-purposed for research while some of the data is collected speci.cally for individual studies.”
BackgroundInfluentialm-Privacy for Collaborative Data Publishing
Goryczka, S., Xiong, L., & Fung, B. C. M. (2011) · International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom) · doi:10.4108/icst.collaboratecom.2011.247094 · Cited by 22
“Privacy preserving data analysis and data publishing [1], [2], [3] have received considerable attention in recent years as promising approaches for sharing data while preserving individual privacy.”
BackgroundMethodInfluentialSelf-citation
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
