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Privacy-preserving data publishing: A survey of recent developments
Open accessAbstract
The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published and on agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this survey, we will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish PPDP from other related problems, and propose future research directions.
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“In particular, sensitive data disclosure is a long-standing challenge in protecting anonymity when data is published and/or analysed [6,111,195].”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
In the citing paper
“Yet, for correlated or otherwise related variables (often referred to as proxies or quasiidentiiers), much of the literature assumes a priori knowledge of the set of quasi-identiiers [111], or seeks to discover them on a case-by-case basis (e.g.[150,217]) towards the idea of privacy preserving data mining [6].”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
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“There is a tension here between advancing the fairness state of the art, privacy [111,268], and policy.”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
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Cited by
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“It may be necessary to protect the sensitive data while publishing with the help of anonymization techniques (Wang et al., 2010).”
Access Control for Data Integration in Presence of Data Dependencies
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k-Indistinguishable Traffic Padding in Web Applications
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“Although l-diversity in PPTP shares the same spirit with that in PPDP, algorithms for PPDP cannot be directly applied here, because in PPDP, many tuples may have the same sensitive values, whereas any action in an action-set is always unique, and we assign a weight for each action to distinguish its possibility to be…”
BackgroundInfluentialA Review of Attribute Disclosure Control
Matwin, S., Nin, J., Sehatkar, M., & Szapiro, T. (2014) · Studies in computational intelligence · doi:10.1007/978-3-319-09885-2_4 · Cited by 16
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Mrissa, M., Tbahriti, S.-E., & Truong, H. (2010) · European Conference on Web Services · doi:10.1109/ecows.2010.11 · Cited by 16
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MethodGame theory based privacy preserving analysis in correlated data publication
Wu, X., Dou, W., & Ni, Q. (2016) · Australasian Computer Science Week Multiconference (ACSW) · doi:10.1145/3014812.3014887 · Cited by 16
“(PPDP) [7] has attracted a lot of attention from academia and industry.”
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“One of the notable privacy violation incidents related to recommendation systems is when Netflix, which it is one of well-known online DVD delivery services, decided to improve its recommendation system in 2006 [37,38] by releasing 100 million tuples contained movie ratings by 500,000 of its users to data analysts.”
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Huang, Y., Milani, M., & Chiang, F. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622249 · Cited by 16
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“For its part, Privacy-Preserving Data Publishing (PPDP) [4] provides a non trusted user with some sanitized data produced by an anonymization process such as k-anonymity, l-diversity or differential privacy to cite a few [4].”
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Gramaglia, M., Fiore, M., Furno, A., et al. (2021) · ACM/IMS Transactions on Data Science · doi:10.1145/3451178 · Cited by 15
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Armando, A., Michele Bezzi, A., Cerbo, F. D., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26148-5_45 · Cited by 15
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Ozalp, I., Gürsoy, M. E., Nergiz, M. E., et al. (2016) · ACM Transactions on Privacy and Security · doi:10.1145/2976738 · Cited by 15
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Shen, Y., Guo, B., Shen, Y. H., et al. (2019) · International Conference on Software Engineering and Information Management (ICSIM) · doi:10.1145/3305160.3305204 · Cited by 15
Anonymization Methods for Privacy-Preserving Data Publishing
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Cuzzocrea, A. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.140 · Cited by 15
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MethodPrivacy Engineering for the Smart Micro-Grid
Pal, R., Hui, P., & Prasanna, V. K. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2846640 · Cited by 15
“A detailed survey of syntactic PPDP mechanisms (including the aforementioned mechanisms) targeting user anonymity can be found in [7] (Table 1 in [7] summarizes somewell-known PPDPmechanisms), none ofwhich provide absolute guarantees of privacy.”
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Sánchez, D., & Viejo, A. (2017) · Online Information Review · doi:10.1108/oir-01-2016-0011 · Cited by 14
McNicholas, C., & Mass, C. F. (2021) · Weather and Forecasting · doi:10.1175/waf-d-20-0222.1 · Cited by 14
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“Another category of attacks aims at achieving the uniformative principle [13].”
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Application of Differential Privacy Approach in Healthcare Data – A Case Study
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BackgroundMethodInfluentialCritical Analysis of Big Data Privacy Preservation Techniques and Challenges
Madan, S., Bhardwaj, K., & Gupta, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-3071-2_23 · Cited by 16
A scalable software solution for anonymizing high-dimensional biomedical data
Meurers, T., Bild, R., Do, K.-M., & Praßer, F. (2021) · GigaScience · doi:10.1093/gigascience/giab068 · Cited by 15
“is sub-tree generalization, which provides a good balance between improved output data quality and interpretability of output datasets [58].”
BackgroundRevisiting distance-based record linkage for privacy-preserving release of statistical datasets
Herranz, J., Nin, J., Rodríguez, P., et al. (2015) · Data & Knowledge Engineering · doi:10.1016/j.datak.2015.07.009 · Cited by 14
“A different scenario is that of data publishing [17], where the data owner collects and stores some original (and sensitive) data T and then releases a modified or perturbed version T ′ of it.”
BackgroundPDA: Semantically Secure Time-Series Data Analytics with Dynamic User Groups
Jung, T., Han, J., & Li, X.-Y. (2016) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2016.2577034 · Cited by 14
“practice is to rely on (1) privacy-preserving data publishing (with sanitization or perturbation) to clean up the sensitive information in the publication [6], [7], or (2) regulation”
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Drosatos, G., Efraimidis, P. S., Williams, G. A., & Kaldoudi, E. (2016) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0005821404720477 · Cited by 14
Preventing Keystroke Based Identification in Open Data Sets
Leinonen, J., Ihantola, P., & Hellas, A. (2017) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3051457.3051458 · Cited by 14
An Improved Data Sanitization Algorithm for Privacy Preserving Medical Data Publishing
Zaman, A., Obimbo, C., & Dara, R. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57351-9_8 · Cited by 14
Utility-aware social network graph anonymization
Ninggal, M. I. H., & Abawajy, J. (2015) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.05.013 · Cited by 14
Distributed Anonymization for Multiple Data Providers in a Cloud System
Ding, X., Yu, Q., Li, J., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37487-6_27 · Cited by 14
Zhu, H. P., Tian, S., Xie, M., et al. (2014) · International Conference on Computer Communication and Networks (ICCCN) · doi:10.1109/icccn.2014.6911855 · Cited by 14
“Sometimes, the publisher does not know how the interested recipients might analyze the data [3].”
BackgroundMethodInfluentialDifferential privacy with δ-neighbourhood for spatial and dynamic datasets
Fang, C., & Chang, E. (2014) · ACM Symposium on Information, Computer and Communications Security · doi:10.1145/2590296.2590320 · Cited by 14
“[10] gives a comprehensive overview on various notions, for example, k-anonymity [20], `-diversity [16], and differential privacy [4].”
BackgroundDecision support for releasing anonymised data
Jändel, M. (2014) · Computers & Security · doi:10.1016/j.cose.2014.07.001 · Cited by 15
“For recent reviews see Agrawal&Yu (2008a) and Fung et al. (2010).”
BackgroundMeasuring Privacy in Online Social Networks
Ananthula, S., Abuzaghleh, O., Alla, N. B., Chaganti, S. P., kaja, P. C., & Mogilineedi, D. (2015) · International Journal of Security Privacy and Trust Management · doi:10.5121/ijsptm.2015.4201 · Cited by 14
“[6] These proposition propose that end users ought to manage the fine-grained strategies with respect to who may see their data.”
BackgroundA Review of Privacy Preserving Federated Learning for Private IoT Analytics.Preprint
Briggs, C. C., Fan, Z., & András, P. (2020) · arXiv (Cornell University) · Cited by 15
Privacy-aware filter-based feature selection
Jafer, Y., Matwin, S., & Sokolova, M. (2014) · 2014 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2014.7004382 · Cited by 14
“disease, salary), and nonsensitive attributes [11].”
BackgroundInfluentialPrivacy protection of medical data in social network
Su, J., Cao, Y., Chen, Y., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01645-0 · Cited by 13
“In this paper, we presented a privacy protection method to release medical data by adopting non-interactive framework [28].”
MethodBackground knowledge attacks in privacy-preserving data publishing models
Desai, N., Das, M. L., Chaudhari, P., et al. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102874 · Cited by 13
Practical anonymization for data streams: z-anonymity and relation with k-anonymity
Jha, N., Vassio, L., Trevisan, M., et al. (2022) · Performance Evaluation · doi:10.1016/j.peva.2022.102329 · Cited by 13
“Thanks to this, Privacy-Preserving Data Publishing (PPDP) has gained attention in the last decade [3].”
BackgroundPrivacy-Preserving Network Embedding Against Private Link Inference Attacks
Han, X. G., Yang, Y., Wang, L., et al. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3264110 · Cited by 13
A Vulnerability Assessment Framework for Privacy-preserving Record Linkage
Vidanage, A., Christen, P., Ranbaduge, T., & Schnell, R. (2023) · ACM Transactions on Privacy and Security · doi:10.1145/3589641 · Cited by 13
“The risk of disclosure of sensitive data is widely discussed in the area of data publishing [24, 26] and statistical disclosure control [18, 23, 58].”
BackgroundFortified MapReduce Layer: Elevating Security and Privacy in Big Data
Gupta, M. K., & Dwivedi, R. K. (2023) · ICST Transactions on Scalable Information Systems · doi:10.4108/eetsis.3859 · Cited by 13
“The randomization process involves the second level [41-45], which is applied to the converted numerical data, further enhancing the privacy level.”
Method{Preserving Privacy of Continuous High-dimensional Data with Minimax Filters}
Hamm, J. (2015) · International Conference on Artificial Intelligence and Statistics · Cited by 13
$${\textsf {SecDM}}$$: privacy-preserving data outsourcing framework with differential privacy
Dagher, G. G., Fung, B. C. M., Mohammed, N., et al. (2019) · Knowledge and Information Systems · doi:10.1007/s10115-019-01405-7 · Cited by 13
Self-citationEnhancing spatial and temporal utilities in differentially private moving objects database release
Deldar, F., & Abadi, M. (2020) · International Journal of Information Security · doi:10.1007/s10207-020-00516-5 · Cited by 13
“On the other hand, anonymized moving objects databases that do not contain personal identifiers or other evidence of identity still do not prevent the precise identification of moving objects [13].”
BackgroundA distributed computing model for big data anonymization in the networks
Ashkouti, F., & Khamforoosh, K. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285212 · Cited by 13
Preserving privacy and frequent sharing patterns for social network data publishing
Fung, B. C. M., Jin, Y., & Li, J. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2492603 · Cited by 13
“We briefly review the related works on thwarting identity disclosures with anonymization technique in the enhanced model [1], which represents the social network data as a graph in which labeled vertices denote participants and their associated information such as jobs and purchased items and edges denote the relation…”
BackgroundSelf-citationPrivacy-preserving data mashup model for trading person-specific information
Khokhar, R. H., Fung, B. C. M., Iqbal, F., et al. (2016) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2016.02.004 · Cited by 13
“(Fung et al., 2010Fung et al. 2010): an adversary attempts to infer the sensitive attribute value in the released dataset using a C4.5 classifier.”
BackgroundMethodInfluentialSelf-citationPrivacy in the age of Pervasive Internet and Big Data Analytics – Challenges and Opportunities
Graduate Student, D. O. C., Punagin, S., & Arya, A. (2015) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2015.07.05 · Cited by 13
“in [50] discuss how Privacy Preserving Data Publishing (PPDP) is a unique problem and address privacy challenges in data publishing while summarizing different approaches to PPDP.”
BackgroundPrivacy Preserving Anonymity for Periodical SRS Data Publishing
Wang, J.-T., & Lin, W.-Y. (2017) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2017.176 · Cited by 13
“In the literature, this situation is called “incremental data publishing” [8].”
InfluentialKanwal, T., Jabbar, A. A., Anjum, A., et al. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719846050 · Cited by 13
An Approach for Distributing Sensitive Values in k-Anonymity
Widodo, W., Budiardjo, E. K., Wibowo, W. C., et al. (2019) · International Workshop on Big Data and Information Security (IWBIS) · doi:10.1109/iwbis.2019.8935849 · Cited by 13
“This table contains three types of attributes which is modified into kanonymity model [20].”
BackgroundA Novel Framework using Apache Spark for Privacy Preservation of Healthcare Big Data
Suneetha, V., Suresh, S., & Jhananie, V. (2020) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia48430.2020.9074867 · Cited by 13
“Anonymization of data is an excellent way to safeguard data privacy[1], and multidimensional anonymization techniques are widely embraced by the current anonymization schemes[2].”
BackgroundWaniek, M., Holme, P., & Rahwan, T. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3148752 · Cited by 13
“Most of the literature puts the responsibility of protecting the system users’ privacy in the hands of a centralized authority [13, 22, 10], but such an authority might be prone to error and negligence.”
BackgroundMansour, H. O., Siraj, M. M., Ghaleb, F. A. A., et al. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/7154705 · Cited by 13
“Three kinds of the disclosure can cause privacy leakage, identity disclosure, attribute disclosure, and membership disclosure [5].”
BackgroundAshkouti, F., Khamforoosh, K., Sheikhahmadi, A., et al. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03958-3 · Cited by 13
“The generalization operator is the most common and powerful operator used in anonymization [17].”
BackgroundInfluentialAllocative and strategic effects of privacy enhancement in smart grids
Kessler, S., Flath, C. M., & Böhm, K. (2014) · Information Systems · doi:10.1016/j.is.2014.09.007 · Cited by 13
ρ-uncertainty Anonymization by Partial Suppression
Jia, X., Pan, C., Xu, X., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05813-9_13 · Cited by 13
“Perturbation is developed for statistical disclosure control [7].”
BackgroundInfluentialDP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization
Jiang, H., Yu, H., Cheng, X., Pei, J., Pless, R. B., & Yu, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3265605 · Cited by 12
“Privacy-preserving data publishing (PPDP) has gained significant attentions in recent years as a promising approach for information sharing while preserving data privacy [2].”
BackgroundUser Privacy Issues in Eucalyptus: A Private Cloud Computing Environment
Waqar, A., Raza, A., & Abbas, H. (2011) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2011.128 · Cited by 12
“As an implementation perspective, formulation of a novel technique or a hybrid of the various already existing techniques based on anonymization, perturbation, or anatomization and permutation [15] are under consideration in our research.”
MethodSecurity and privacy architectures for biomedical cloud computing
Danilatou, V., & Ioannidis, S. (2010) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2010.5687691 · Cited by 12
“in [12] present a survey of of recent advances in privacy-preserving data publishing.”
BackgroundData Anonymization With Diversity Constraints
Milani, M., Huang, Y., & Chiang, F. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3131528 · Cited by 12
“We show that ð k; S Þ -anonymization is NP-hard but the decision problem is in PTIME.”
BackgroundMethodInfluentialQuantifying the Effects of Anonymization Techniques Over Micro-Databases
Sadhya, D., & Chakraborty, B. (2022) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2022.3141754 · Cited by 12
“These procedures are collectively known in the literature as sanitization mechanisms [1].”
BackgroundInfluentialMANCHINI, C. E. F., Ospina, R., Leiva, V., & Martín-Barreiro, C. (2022) · Information Sciences · doi:10.1016/j.ins.2022.10.076 · Cited by 12
Optimized key generation-based privacy preserving data mining model for secure data publishing
Kulkarni, Y. R., Jagdale, B. N., & Sugave, S. R. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103332 · Cited by 12
DataSifterText: Partially Synthetic Text Generation for Sensitive Clinical Notes
Zhou, N., Wu, Q., Wu, Z., et al. (2022) · Journal of Medical Systems · doi:10.1007/s10916-022-01880-6 · Cited by 12
A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IOT
Chen, Q., Ye, A., Zhang, Q., et al. (2023) · Chinese Journal of Electronics · doi:10.23919/cje.2021.00.411 · Cited by 12
The Effect of Homogeneity on the Complexity of k-Anonymity
Bredereck, R., Nichterlein, A., Niedermeier, R., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22953-4_5 · Cited by 12
“In particular, note that in the data privacy community “differential privacy” (cleverly adding some random noise) is now the most popular method [9,14].”
Dyadic product and crow lion algorithm based coefficient generation for privacy protection on cloud
George, A., & Sumathi, A. C. (2018) · Cluster Computing · doi:10.1007/s10586-017-1589-6 · Cited by 12
“However, the publisher will not transit the trust to the data recipient [18].”
Set-valued Data Anonymization Maintaining Data Utility and Data Property
Gunawan, D., & Mambo, M. (2018) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1145/3164541.3164583 · Cited by 12
“These approaches result in less data distortion and it reduces data utility, but unfortunately flexibility leads data exploration problem[8].”
BackgroundPrivacy and Ethical Challenges in Big Data
Gambs, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-18419-3_2 · Cited by 14
Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing
Widodo, W., Budiardjo, E. K., & Wibowo, W. C. (2019) · Information · doi:10.3390/info10120362 · Cited by 12
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
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