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 601–700 of 1,001
Balancing Utility and Fairness against Privacy in Medical Data
Chester, A., Koh, Y. S., Wicker, J. S., Sun, Q., & Lee, J. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308226 · Cited by 12
“Thus, if the operation that gains the most information also reduces the privacy to the extent that no further specialisation operation can be performed then the final result may be sub-optimal [2].”
BackgroundMethodMulti-Level Privacy Preserving K-Anonymity
Weng, J.-H., & Chi, P.-W. (2021) · Asia Joint Conference on Information Security (AsiaJCIS) · doi:10.1109/asiajcis53848.2021.00019 · Cited by 12
Quantifying Privacy: A Novel Entropy-Based Measure of Disclosure Risk
Alfalayleh, M., & Branković, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19315-1_3 · Cited by 12
“There are various Statistical Disclosure Control (SDC) techniques that can be used to alleviate this problem [40, 2, 5, 13, 20] but, unfortunately, none of them is able to solve it completely, due to its intrinsic contradictory nature.”
BackgroundThe Independence of Fairness-Aware Classifiers
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2013) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2013.133 · Cited by 12
“Further, FADM has connection with a notion of t-closeness [24] in terms of the prevention of disclosing specific information when disclosing distinct information.”
BackgroundAdaptive, secure, and scalable distributed data outsourcing
Xiong, L., Goryczka, S., & Sunderam, V. S. (2011) · doi:10.1145/1996010.1996012 · Cited by 12
“Most literature [22] following the seminal work on k-anonymity [41, 45] and l-diversity [33] adopts relaxed privacy notions for the non-interactive model by considering specific attacks and assuming the attacker has limited background knowledge.”
BackgroundPrivacy preserving data publishing: Review
Rashid, A. H., & Norizan, B. M. Y. (2015) · International Journal of the Physical Sciences · doi:10.5897/ijps11.1795 · Cited by 12
Whom to Trust? Using Technology to Enforce Privacy
Métayer, D. L. (2016) · Law, governance and technology series · doi:10.1007/978-3-319-25047-2_17 · Cited by 12
Automatic detection of sensitive attribute in PPDM
Kamakshi, P., & Babu, A. V. (2012) · IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) · doi:10.1109/iccic.2012.6510183 · Cited by 12
Semantic Disclosure Control: semantics meets data privacy
Batet, M., & Sánchez, D. (2018) · Online Information Review · doi:10.1108/oir-03-2017-0090 · Cited by 12
Privacy preserving serial publication of transactional data
Bewong, M., Liu, J., Liu, L., et al. (2019) · Information Systems · doi:10.1016/j.is.2019.01.001 · Cited by 12
Fast, V., Schnurr, D., & Wohlfarth, M. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3759664 · Cited by 12
Data science and AI in FinTech: An overviewPreprint
Cao, L., Yang, Q., & Yu, P. S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3890556 · Cited by 13
Self-citationZhang, T., Yang, G., & Ye, D. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3411803 · Cited by 11
“Many privacy-preserving approaches, such as differential privacy and k -anonymity, are rapidly developed to improve cyber security [23].”
The Effect of Clustering on Data Privacy
Canbay, P., & Sever, H. (2015) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2015.198 · Cited by 11
“Therefore, to measure the diversity of anonymized dataset, the equivalence class numbers are used.”
MethodRisk-Based Privacy-Aware Access Control for Threat Detection Systems
Metoui, N., Michele Bezzi, A., & Armando, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-662-56266-6_1 · Cited by 11
“2 Other privacy metrics exist (for example, -diversity, and t-closeness, see [19] for a survey), but k-anonymity is still a de-facto standard in real-world applications.”
BackgroundKnowledge Based Access Control a model for security and privacy in the Big Data
Haourani, L. E., Kalam, A. A. E., & Ouahman, A. A. (2018) · International Conference on Smart City Applications (SCA) · doi:10.1145/3286606.3286793 · Cited by 11
“Samarati [22], this concept states that the sensitive data are deleted or generalized until each line is identical to at least k-1 other lines, the K-annonymat uses two techniques "deletion, and generalization" for some values of K. Privacy Enhancing Technologies (PET) are tools, applications and…”
MethodEnabling Privacy-Preserving Sharing of Cyber Threat Information in the Cloud
Fan, W., Ziembicka, J., de Lemos, R., et al. (2019) · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) · doi:10.1109/cscloud/edgecom.2019.00-15 · Cited by 11
“Hence, in order to get a trade-off between the data utility and privacy, the privacy-preserving data publishing approach [14] for data sharing is a vitally important point.”
BackgroundFully Homomorphic based Privacy-Preserving Distributed Expectation Maximization on Cloud
Alabdulatif, A., Khalil, I., Zomaya, A. Y., et al. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2999407 · Cited by 11
“They also rely on a trusted third party (TTP), which represents a security vulnerability that could reveal users’ private information [14].”
BackgroundA secure architecture for exploring patient-level databases from distributed institutions
Almeida, J. R., Barraca, J. P., & Oliveira, J. L. (2022) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms55023.2022.00086 · Cited by 11
Probabilistic km-anonymity efficient anonymization of large set-valued datasets
Ács, G., Achara, J. P., & Castelluccia, C. (2015) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2015.7363870 · Cited by 11
Privacy Preserving Social Network Publication Against Mutual Friend Attacks
Sun, C., Yu, P. S., Kong, X., et al. (2015) · Transactions on data privacy · doi:10.5555/2870621.2870622 · Cited by 11
Self-citationSpeeding up Privacy Preserving Record Linkage for Metric Space Similarity Measures
Sehili, Z., & Rahm, E. (2016) · Datenbank-Spektrum · doi:10.1007/s13222-016-0222-9 · Cited by 11
“For this purpose, a large number of techniques has been proposed in particular for privacy-preserving publishing, record linkage and data mining of sensitive data [20, 21, 9].”
BackgroundSecure principal component analysis in multiple distributed nodes
Won, H., Kim, S., Lee, S., Choi, M., & Moon, Y. (2016) · Security and Communication Networks · doi:10.1002/sec.1501 · Cited by 10
“Here, ̆ is the scalar product of two vectors [4,9,11,29], and SSDD exploits its SMC version, called the secure scalar product.”
BackgroundPrivacy Preserving Big Data Publishing
Victor, N., & Lopez, D. (2017) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-5225-2863-0.ch003 · Cited by 11
On the Privacy and Utility Properties of Triple Matrix-Masking
Ding, A. A., Miao, G., & Wu, S. S. (2020) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.674 · Cited by 11
“Such information can enable adversaries to launch probabilistic attacks [Machanavajjhala et al., 2007, Fung et al., 2010].”
BackgroundPPTPF: Privacy-Preserving Trajectory Publication Framework for CDR Mobile Trajectories
Yang, J., Dash, M., & Teo, S. G. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10040224 · Cited by 11
Privacy Preserving Text Representation Learning Using BERT
Alnasser, W., Beigi, G., & Liu, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80387-2_9 · Cited by 11
Yan, Z., & Chakraborty, D. (2014) · Synthesis lectures on the semantic web · doi:10.2200/s00577ed1v01y201404wbe008 · Cited by 11
Xia, W., Kantarcıoğlu, M., Wan, Z., Heatherly, R. D., Vorobeychik, Y., & Malin, B. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806580 · Cited by 12
“…that privacy can be compromised when a record is linked to the individual from whom it was derived (often referred to as identity disclosure ) [10], the inference of a sensitive value associated with the corresponding individual (often referred to as attribute disclosure ) [16], the ability…”
BackgroundJung, K., Park, S., & Park, S. (2014) · doi:10.1145/2663715.2669611 · Cited by 11
Key Research Issues for Privacy Protection and Preservation in Cloud Computing
Zhang, G., Yang, Y. J., Zhang, X., et al. (2012) · International Conference on Cloud and Green Computing · doi:10.1109/cgc.2012.47 · Cited by 11
(l, e)-Diversity – A Privacy Preserving Model to Resist Semantic Similarity Attack
Wang, H., Han, J., Wang, J., et al. (2014) · Journal of Computers · doi:10.4304/jcp.9.1.59-64 · Cited by 11
Decision Trees and Random Forest for Privacy-Preserving Data Mining
Szücs, G. (2013) · Advances in e-business research series · doi:10.4018/978-1-4666-4181-5.ch004 · Cited by 11
Data Protection Method Against Cross-Domain Inference Threat in Cyber–Physical Power Grid
Hu, B., Zhou, Y., Zhou, Y., Wu, J., Liu, T., & Guan, X. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3363049 · Cited by 10
“After that, abundant data protection methods based on differential privacy are developed [21], [22], [23], [24].”
MethodKara, B. C., Eyüpoğlu, C., & Karakuş, O. (2025) · IEEE Access · doi:10.1109/access.2025.3559410 · Cited by 13
“Some research on trade-off metrics have been published in the literature [36].”
Using Patterns to Form Homogeneous Teams
Bredereck, R., Köhler, T., Nichterlein, A., et al. (2013) · Algorithmica · doi:10.1007/s00453-013-9821-0 · Cited by 10
“Notably, the concept of homogeneity in teams is similar to the concept of k-anonymity in privacy-preserving data publishing [16].”
Achieving Probabilistic Anonymity in a Linear and Hybrid Randomization Model
Sang, Y., Shen, H., Tian, H., et al. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2562605 · Cited by 10
“Determining an optimal solution for k-anonymity with minimal information loss is very time consuming.”
BackgroundDe, S. J., & Métayer, D. L. (2016) · Synthesis lectures on information security, privacy, and trust · doi:10.1007/978-3-031-02349-1 · Cited by 10
Khokhar, R. H., Iqbal, F., Fung, B. C. M., et al. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2020.2974210 · Cited by 10
“This approach is widely known as privacy-preserving data publishing (PPDP) [30], which is more appropriate in many real-life data sharing scenarios because of the flexibility for a data analyst to perform an analysis without back and forth querying of the database.”
BackgroundInfluentialSelf-citationGraph-based managing and mining of processes and data in the domain of intellectual property
Hübscher, G., Geist, V., Auer, D., Ekelhart, A., Mayer, R. L., Nadschläger, S., & Küng, J. (2021) · Information Systems · doi:10.1016/j.is.2021.101844 · Cited by 11
“For a detailed overview on privacy-preserving methods, see [45, 46]. k-anonymity aims to ensure that for a given dataset, there are at least k data objects (rows) that are indistinguishable regarding their quasi-identifiers.”
BackgroundPrivacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
Rodríguez-García, M., Balderas, A., & Dodero, J. M. (2021) · Applied Sciences · doi:10.3390/app11188506 · Cited by 10
Privacy-Preserving Collaborative Learning Through Feature Extraction
Sarmadi, A., Fu, H., Krishnamurthy, P., Garg, S., & Khorrami, F. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3263507 · Cited by 12
“Another approach, referred to as Privacy Preserving Data Publishing (PPDP) [23], sanitizes (or anonymizes) data before passing it to other parties in a way that the anonymized data itself does not reveal sensitive information of individuals.”
BackgroundInfluentialPriView: Personalized Media Consumption Meets Privacy against Inference Attacks
Bhamidipati, S., Fawaz, N., Kveton, B., et al. (2015) · IEEE Software · doi:10.1109/ms.2015.100 · Cited by 10
A privacy-enhancing model for location-based personalized recommendations
Huang, J., Qi, J., Xu, Y., et al. (2014) · Distributed and Parallel Databases · doi:10.1007/s10619-014-7148-8 · Cited by 10
“In privacy preserving data publishing [7,21,25], a trusted party, called publisher, collects all data (i.”
BackgroundPREDICT: Privacy and Security Enhancing Dynamic Information Collection and Monitoring
Xiong, L., Sunderam, V. S., Fan, L., et al. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.367 · Cited by 10
“Traditional approaches such as removing identifying attributes, generalizing or perturbing individual attribute values, have been shown to be susceptible to various attacks [20].”
BackgroundSimilarity matching for uncertain time series
Dallachiesa, M., Nushi, B., Мирыленка, К., et al. (2011) · doi:10.1145/2064969.2064971 · Cited by 10
“In these applications, privacy is a major concern, and it can be ensured by different privacy-preserving transforms [2, 10, 16], namely, noisy perturbations, noisy aggregates, and reduced granularity.”
BackgroundPrivacy-Preserving Data Publishing for Free Text Chinese Electronic Medical Records
Chen, L., Yang, J., & Wang, Q. (2012) · IEEE Annual Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2012.76 · Cited by 10
Alwatban, I. S., & Emam, A. Z. (2013) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213014500043 · Cited by 10
Meerkat - A Dynamic Privacy Framework for Web Services
Tbahriti, S.-E., Medjahed, B., Malik, Z., et al. (2011) · IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2011.38 · Cited by 10
“Meerkat A Dynamic Privacy Framework for Web Services Salah-Eddine Tbahriti1 Brahim Medjahed2 Zaki Malik3 Chirine Ghedira1 Michael Mrissa1 1 University de Lyon 2 University of Michigan-Dearborn 3 Wayne State University LIRIS-CNRS Department of Computer and Information Science Department of Computer Science Lyon, France…”
MethodFusion: Privacy-Preserving Distributed Protocol for High-Dimensional Data Mashup
Dagher, G. G., Iqbal, F., Arafati, M., et al. (2015) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2015.100 · Cited by 10
Self-citationToward Scalable Anonymization for Privacy-Preserving Big Data Publishing
Mehta, B. B., & Rao, U. P. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-8636-6_31 · Cited by 10
Rajput, K., Chetty, G., & Davey, R. (2018) · Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) · doi:10.1109/apwconcse.2018.00017 · Cited by 10
Mehta, B. B., Gupta, R., Rao, U. P., & Muthiyan, M. (2019) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt45670.2019.8944778 · Cited by 10
“[14] has given a detailed survey of privacy preserving data publishing approaches.”
BackgroundGenerating Electronic Health Records with Multiple Data Types and Constraints
Yan, C., Zhang, Z., Nyemba, S., & Malin, B. (2020) · PubMed · doi:10.48550/arxiv.2003.07904 · Cited by 10
“3,4 Various computational approaches have been proposed to maintain patient anonymity and confidentiality 5 , but providing raw data, at any degree of specificity, leads to a tradeoff between privacy and data utility.”
BackgroundSecure Multi-party Summation Protocols: Are They Secure Enough Under Collusion?
Ranbaduge, T., Vatsalan, D., & Christen, P. (2020) · ANU Open Research (Australian National University) · Cited by 10
Protecting Privacy When Sharing and Releasing Data with Multiple Records per Person
Kartal, H., & Li, X. (2020) · Journal of the Association for Information Systems · doi:10.17705/1jais.00643 · Cited by 10
z-anonymity: Zero-Delay Anonymization for Data Streams
Jha, N., Favale, T., Vassio, L., et al. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378422 · Cited by 10
“Thanks to this, Privacy-Preserving Data Publishing (PPDP) has gained attention in the last decade [2].”
BackgroundChen, R., Akkuş, İ. E., & Francis, P. (2013) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2534169.2486013 · Cited by 10
Self-citationSupervised and unsupervised data mining approaches in loan default prediction
Alejandrino, J. C., Bolacoy, J. J. P., & Murcia, J. V. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i2.pp1837-1847 · Cited by 10
Attack Vector Analysis and Privacy-Preserving Social Network Data Publishing
Ninggal, M. I. H., & Abawajy, J. (2011) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2011.113 · Cited by 10
“The focus of this paper is to study privacypreserving mechanism for social network data.”
BackgroundGraph Anonymization Using Machine Learning
Maag, M. L., Denoyer, L., & Gallinari, P. (2014) · IEEE International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2014.20 · Cited by 10
“Techniques to anonymize data for which each record represents a separate entity (as in a relational database) have been developed and are currently used (e.g. Fung et al. [5]).”
MethodOn privacy-preserving publishing of spontaneous ADE reporting data
Lin, W.-Y., & Yang, D.-C. (2013) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2013.6732760 · Cited by 10
“Due to space limit, all references of the models mentioned in [3] are omitted in this paper.”
BackgroundResultInfluentialA privacy-preserving data collection model for digital community
Li, H., Ma, J., & Fu, S. (2014) · Science China Information Sciences · doi:10.1007/s11432-014-5197-2 · Cited by 10
Scalable Iterative Classification for Sanitizing Large-Scale Datasets
Li, B., Vorobeychik, Y., Li, M., & Malin, B. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2628180 · Cited by 10
“To accomplish such goals, the past several decades have brought forth the development of numerous data protection models [5].”
BackgroundPreserving Privacy in Big Data: A Survey from the Cryptographic Perspective
Huang, L., Tian, M., & Huang, H. (2015) · Cited by 10
A clustering approach for anonymizing distributed data streams
Mohamed, M. A., Nagi, M. H., & Ghanem, S. M. (2016) · International Conference on Computer Engineering & Systems (ICCES) · doi:10.1109/icces.2016.7821968 · Cited by 10
“Privacy techniques are presented in [19][20], and the notions of k-anonymity and quasi-identifiers are stated.”
BackgroundA relative privacy model for effective privacy preservation in transactional data
Bewong, M., Liu, J., Liu, L., et al. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4923 · Cited by 10
On Consistency in Multiquestion Negative Surveys With Application to Healthcare Data Collection
Jiang, H., & Luo, W. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2900714 · Cited by 10
“However, these techniques still have some deficiencies as mentioned in [20], and these techniques could not be suitable for big-survey-data scenarios because their computational complexity is high [24], [25].”
BackgroundMethodOn the k-Anonymization of Time-Varying and Multi-Layer Social Graphs
Rossi, L., Musolesi, M., & Andrea Torsello, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14605 · Cited by 10
“The assignment step , on the other hand, requires solving a Generalized Assignment Problem (GAP) with lower bounds (Hamada, Iwama, and Miyazaki 2011; Krumke and Thielen 2013).”
BackgroundDu, W. K., Eppstein, D., Goodrich, M. T., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03367-4_22 · Cited by 10
Privacy-aware data management by means of data degradation
van Heerde, H. J. W. (2010) · Cited by 10
Improving Suppression to Reduce Disclosure Risk and Enhance Data UtilityPreprint
Orooji, M., & Knapp, G. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00716 · Cited by 10
“Some surveys have reviewed different privacy models, anonymization operations, privacy techniques, and data utility metrics [10, 11].”
BackgroundPrivacy Preserving Text Analytics
Veluru, S., Rahulamathavan, Y., Gupta, B. B., et al. (2014) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-4666-6559-0.ch017 · Cited by 9
k-anonymity Based Privacy Preserving for Data Collection in Wireless Sensor Networks
Kaliyamurthie, K. P., Parameswari, D. D. V. L., & Udayakumar, R. (2013) · doi:10.17485/ijst/2013/v6i5s/33362 · Cited by 9
Employing privacy policies and preferences in modern e-government environments
Drogkaris, P., Gritzalis, S., & Lambrinoudakis, C. (2013) · International Journal of Electronic Governance · doi:10.1504/ijeg.2013.058361 · Cited by 9
“In an attempt to improve data and process interoperability, XML schemas have been widely adopted by e-government environments and their corresponding interoperability frameworks (e-GIF’s) (Lee et al., 2009; Guijarro, 2007; Fung et al., 2010).”
BackgroundA Refined Complexity Analysis of Degree Anonymization in Graphs
Hartung, S., Nichterlein, A., Niedermeier, R., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39212-2_52 · Cited by 9
Islami, L., Fischer‐Hübner, S., & Papadimitratos, P. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102913 · Cited by 9
Privacy-Preserved Data Trading Via Verifiable Data Disturbance
Zhang, M., Li, X., Ren, Y., et al. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3323669 · Cited by 9
“However, considering the privacy leakage [15], DOs are re-luctant to trade their data containing privacy information [16].”
BackgroundPRIMϵ: Novel Privacy-Preservation Model With Pattern Mining and Genetic Algorithm
Madhusudhanan, S., Jose, A. C., Sahoo, J., & Malekian, R. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3324769 · Cited by 9
“Therefore, publishing datasets must restrain from spurting sensitive information by adhering to Privacy Preservation in Data Publishing (PPDP) [14], [15].”
BackgroundPatil, R., & Patil, P. D. (2024) · World Wide Web · doi:10.1007/s11280-024-01244-9 · Cited by 9
A Systematic Mapping Study on Social Network Privacy: Threats and Solutions
Rodrigues, A., Villela, M. L. B., & Feitosa, E. L. (2024) · ACM Computing Surveys · doi:10.1145/3645086 · Cited by 9
Advances in Differential Privacy and Differentially Private Machine Learning
Das, S., & Mishra, S. (2024) · Springer tracts in electrical and electronics engineering · doi:10.1007/978-981-97-0407-1_7 · Cited by 9
“Zhao and Chen (2021)[128] discuss various applications of differential privacy for privacy preserving analysis of unstructured data, viz. images, video, audio etc. Surveys by Jiang et al[58] and Fung et al[43] discuss location privacy and privacy-preserving data publishing respectively, and include brief discussions o…”
BackgroundFrom Principle to Practice: Vertical Data Minimization for Machine Learning
Staab, R., Jovanović, N. M., Balunović, M., et al. (2024) · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · doi:10.1109/sp54263.2024.00089 · Cited by 9
Differentially Private Data Release: Improving Utility with Wavelets and Bayesian Networks
Xiao, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11116-2_3 · Cited by 9
“This has motivated numerous data publishing techniques (see [1, 7, 9] for surveys) that aim to provide better privacy protection based on formal models of privacy requirements.”
BackgroundA Scalable K-Anonymization Solution for Preserving Privacy in an Aging-in-Place Welfare Intercloud
Chakravorty, A., Wlodarczyk, T. W., & Rong, C. (2014) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2014.43 · Cited by 9
“Several centralized k-anonymization algorithms are discussed in literature as surveyed in [13].”
MethodPrivacy Preserving Data Publishing: A Classification Perspective
N, A., & Obimbo, C. (2014) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2014.050919 · Cited by 9
“Researchers have proposed many algorithms for Privacy Preserving Data Mining (PPDM) and PPDP, however, not much is found in literature that addresses the privacy preservation to achieve the goal of classification [5].”
MethodZhang, X., Liu, C., Chen, J., et al. (2011) · IEEE International Conference on Dependable, Autonomic and Secure Computing · doi:10.1109/dasc.2011.98 · Cited by 9
“To calculate the hiding cost of intermediate datasets, some vital attributes of a single intermediate datasets should be defined.”
BackgroundA Survey on Privacy Preservation RecentApproaches and Techniques
Dhivakar, K., & Mohana, S. (2014) · International Journal of Innovative Research in Computer and Communication Engineering · Cited by 9
Sensor data anonymization based on genetic algorithm clustering with L-Diversity
Ainur, A., & Spivak, A. (2016) · doi:10.1109/fruct-ispit.2016.7561500 · Cited by 9
LRDM: Local Record-Driving Mechanism for Big Data Privacy Preservation in Social Networks
Li, W., & Li, H. (2016) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2016.94 · Cited by 9
“Thirdly, A variety of privacy models and data anonymization approaches have been proposed and extensively studied recently [1], [7], [13].”
BackgroundA New Data Collection Technique for Preserving Privacy
Wu, S. S., Chen, S., Burr, D. L., et al. (2018) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v7i3.408 · Cited by 9
“There are several other methods that are designed with the intention to collect data anonymously without revealing the providers’ identities, including various cryptographic solutions (Yang et al., 2005; Gehrke, 2006; Fung et al., 2010) and anonymous communications (Chaum, 1981; Jakobsson et al.”
An Intra-slice Chaotic-Based Security Solution for Privacy Preservation in Future 5G Systems
Mareca, P., & Bordel, B. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77712-2_14 · Cited by 9
Privacy-Preserving Ontology Publishing for $$\mathcal {EL} $$ Instance Stores
Baader, F., Kriegel, F., & Nuradiansyah, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-19570-0_21 · Cited by 9
Multi-View Low-Rank Coding-Based Network Data De-Anonymization
Xian, X., Wu, T., Qiao, S., Wang, W., Liu, Y., & Han, N. (2020) · IEEE Access · doi:10.1109/access.2020.2995568 · Cited by 10
“Therefore, privacy preserving has become an indispensable task for network data publication [11].”
BackgroundData-sharing markets for integrating IoT data processing functionalities
Kasrin, N., Benabbas, A., Elmamooz, G., Nicklas, D., Steuer, S., & Sünkel, M. (2021) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-020-00054-y · Cited by 9
Rodríguez-García, M., Sicilia, M., & Dodero, J. M. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.568 · Cited by 9
“These provable privacy guarantees can be attained for a particular dataset using one or several PPDPmethods (Fung et al., 2010;Hundepool et al., 2012), as summarized in Table 2.”
BackgroundInvestigating Customers’ Perceived Sensitivity of Information Shared with a Robot Bartender
Rossi, A., Perugia, G., & Rossi, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90525-5_11 · Cited by 9
Correlated Differential Privacy of Multiparty Data Release in Machine Learning
Zhao, J., Wang, X., Mao, K., et al. (2022) · Journal of Computer Science and Technology · doi:10.1007/s11390-021-1754-5 · Cited by 9
Sowmyarani, C. N., Namya, L. G., Nidhi, G. K., et al. (2022) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct55679.2022.9865682 · Cited by 9
Sensitive Disclosures under Differential Privacy Guarantees
Han, C., & Wang, K. (2015) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2015.25 · Cited by 9
“Numerous methods have been proposed to protect data privacy, see [35, 4, 10] for surveys.”
MethodSelf-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
