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Privacy-preserving data publishing: A survey of recent developments
Open accessAbstract
The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published and on agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this survey, we will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish PPDP from other related problems, and propose future research directions.
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“In particular, sensitive data disclosure is a long-standing challenge in protecting anonymity when data is published and/or analysed [6,111,195].”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
In the citing paper
“Yet, for correlated or otherwise related variables (often referred to as proxies or quasiidentiiers), much of the literature assumes a priori knowledge of the set of quasi-identiiers [111], or seeks to discover them on a case-by-case basis (e.g.[150,217]) towards the idea of privacy preserving data mining [6].”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
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“There is a tension here between advancing the fairness state of the art, privacy [111,268], and policy.”
From Fairness in machine learning: A survey (2024) · cites this paper as [111]
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Items 901–1,000 of 1,001
Efficient algorithms for identifying privacy vulnerabilities
Gkoulalas-Divanis, A., & Braghin, S. (2015) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc2.2015.7366170 · Cited by 5
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A Hybrid Optimization Approach for Anonymizing Transactional Data
Wang, L.-E., & Li, X. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27161-3_11 · Cited by 5
“Due to the high dimensionality and sparsity of transactional data, many methodologies have been proposed to protect the privacy of published transactional data including Generalization which operates by mapping original items to generalized items [4–8] and Suppression which removes items before releasing data [3], Buc…”
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Bewong, M., Liu, J., Liu, L., et al. (2017) · doi:10.1109/trustcom/bigdatase/icess.2017.263 · Cited by 5
Personal Analytics and Privacy. An Individual and Collective Perspective
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“, [5] for an extended survey of such techniques).”
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“[13, 24], it is only relatively recently that research has advanced in the field of sensitivity classifica-”
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Rajput, K., Chetty, G., & Davey, R. (2018) · doi:10.1109/icmlde.2018.00033 · Cited by 5
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Abdelhameed, S. A., Moussa, S. M., Badr, N. L., & Khalifa, M. E. (2021) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis52592.2021.9694174 · Cited by 5
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(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.b1087.1292s19 · Cited by 5
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Location Privacy in Mobile Big Data: User Identifiability via Habitat Region Representation
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Allard, T., Nguyen, B., & Pucheral, P. (2010) · Cited by 5
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BackgroundModelling of a privacy language and efficient policy-based de-identification
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Vatsalan, D. (2014) · ANU Open Research (Australian National University) · doi:10.25911/5d739004a7846 · Cited by 5
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Tachioka, Y. (2022) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scisisis55246.2022.10002081 · Cited by 4
“A data-anonymization method that preserves privacy of data while maintaining utility is called privacy-preserving data mining [1], [2] or privacy-preserving data publishing [3].”
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Monteiro, M., Correia, F. F., Queiroz, P. G. G., et al. (2024) · doi:10.1145/3698322.3698337 · Cited by 4
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Khan, A., Setiawan, J. W., Paing, S. N., Duong, T. Q., Win, M. Z., & Shin, H. (2025) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2025.3568015 · Cited by 4
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Privacy-Preserving Data Sharing Platform
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Enhancing efficiency and data utility in longitudinal data anonymization
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“It is often achieved through a mechanism called generalization (see [3] for a good overview).”
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Anciaux, N., Boutara, D., Nguyen, B., et al. (2015) · Fundamenta Informaticae · doi:10.3233/fi-2015-1176 · Cited by 4
KAMP: Preserving k-Anonymity for Combinations of Patterns
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BackgroundSupporting data aspects in pig latin
Dyreson, C., Florez, O. U., Thakre, A., et al. (2013) · International Conference on Aspect-Oriented Software Development (AOSD) · doi:10.1145/2451436.2451439 · Cited by 4
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BackgroundClassification of commercial and personal profiles on MySpace
Gauvin, W., Chen, C., Fu, X., et al. (2011) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2011.5766883 · Cited by 4
“The algorithm is then applied to a Privacy-Preserving Data Publishing (PPDP) service to provide anonymity for online social network users.”
BackgroundInfluentialPreserving organizational privacy in intrusion detection log sharing
Bahşi, H., & Levi, A. W. (2011) · Cited by 4
Statistical Database Auditing Without Query Denial Threat
Lu, H., Vaidya, J. S., Atluri, V., et al. (2014) · INFORMS journal on computing · doi:10.1287/ijoc.2014.0607 · Cited by 4
“A survey of current advancements on privacy in data publishing, e.g., k-anonymity (Samarati and Sweeney 1998), l-diversity (Machanavajjhala et al. 2006), t-closeness (Li et al. 2007), and differential privacy (Dwork 2008), can be found in Fung et al. (2010).”
BackgroundPrivacy Protected Data Forwarding in Human Associated Delay Tolerant Networks
Gao, L., Li, M., Zhou, W., et al. (2013) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2013.72 · Cited by 4
“And some modifications need to be applied by a sequence of anonymization operations to the quasi-identifiers [9].”
BackgroundPreserving Privacy in Outsourced Database
Lin, Y.-W., Yang, L.-C., Lin, L.-C., et al. (2014) · International Journal of Computer and Communication Engineering · doi:10.7763/ijcce.2014.v3.350 · Cited by 4
SPPM: sparse privacy preserving mappings
Salamatian, S., Fawaz, N., Kveton, B., et al. (2014) · Cited by 4
Data Protection Issues of Integrated Electronic Health Records (EHR)
Schiza, E. C., Fakas, G. J., Pattichis, C. S., et al. (2016) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-32703-7_153 · Cited by 4
Machine Learning Solutions in Computer-Aided Medical Diagnosis
Belciug, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50478-0_14 · Cited by 4
Privacy Preservation for Trajectory Data Publishing by Look-Up Table Generalization
Harnsamut, N., Natwichai, J., & Riyana, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92013-9_2 · Cited by 4
Educational Sensitive Information Retrieval: Analysis, Application, and Optimization
Wang, X., & Wang, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2844158 · Cited by 4
K-anonymity model for privacy-preserving soccer fitness data publishing
Li, R., An, S., Li, D., et al. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201818903007 · Cited by 4
“The data mining community focused on developing techniques that would enable data utility while preserving the privacy of individuals and started a popular branch of research named “Privacy Preserving Data Publishing” [1].”
MethodData Privacy Quantification and De-identification Model Based on Information Theory
Zhang, Z., Lu, Z., & Tian, Y. (2019) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana.2019.00046 · Cited by 4
“In order to deal with the issue of privacy leakage, privacy-preserving data publishing(PPDP) provides methods and tools for publishing useful information while preserving data privacy [4].”
MethodFlexible sensitive K-anonymization on transactions
Tsai, Y., Wang, S.-L., Ting, I., et al. (2020) · World Wide Web · doi:10.1007/s11280-020-00798-8 · Cited by 4
Detecting Privacy Violations in Multiple Views Publishing
Dou, D., & Coulondre, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32597-7_46 · Cited by 4
“We refer readers to [3] for a survey of different privacy preserving approaches with respect to privacy-preserving data publishing.”
BackgroundUser-Generated Pseudonyms Through Merkle Trees
Kermezis, G., Limniotis, K., & Kolokotronis, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-76663-4_5 · Cited by 4
Técnicas de Privacidade de Dados de Localização
Machado, J. C., Neto, E. R. D., & Filho, M. E. B. (2019) · doi:10.5753/sbc.6251.1.1 · Cited by 4
“A aborda-gem mais promissora para solucionar o problema da preservação de privacidade consiste em anonimizar os dados antes de sua liberação para uso [15], visando impedir a exposição de dados sensíveis dos indivíduos.”
BackgroundThe Impact of Privacy Regulations on DB Systems
Machado, J. C., & Amora, P. R. P. (2021) · Journal of Information and Data Management · doi:10.5753/jidm.2021.1958 · Cited by 4
“They apply data transformation based on generalization, suppression, or perturbation to make obscure property values that otherwise would be easier to single out an individual on a dataset [Fung et al. 2010].”
MethodDomingo‐Ferrer, J., Sánchez, D., & Hajian, S. (2015) · Computer communications and networks · doi:10.1007/978-3-319-08470-1_2 · Cited by 4
PGAN framework for synthesizing sensor data privately
Menasria, S., Lu, M., & Dahou, A. (2022) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2022.103204 · Cited by 5
k-Anonymity on Metagenomic Features in Microbiome Databases
Mayer, R. L., Karlowicz, A., & Hittmeir, M. (2023) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3600160.3600178 · Cited by 4
“Privacy-preserving data publishing (PPDP) [7] deals with providing data that can be shared with other parties, without infringing the privacy of contained individuals.”
BackgroundData Preserving Techniques for Collaborative Data Publishing
Indhumathi, R., & Mohana, S. (2013) · Cited by 4
What Private Information Are You Disclosing? A Privacy-Preserving System Supervised by Yourself
Celdrán, A. H., Pérez, M. G., Clemente, F. J. G., et al. (2014) · doi:10.1109/hpcc.2014.199 · Cited by 4
“INTRODUCTION How to preserve the privacy of users’ information is an open challenge that has been extensively studied in multiple application domains [1].”
BackgroundRamachandran, S., Chithan, S., & Ravindran, S. (2014) · International Conference on Recent Trends in Information Technology · doi:10.1109/icrtit.2014.6996145 · Cited by 4
Storage cost efficient approach for cloud storage and intermediate data sets in clouds
Khare, M. D., Tripathi, A., & Singh, P. K. (2015) · International Conference on Green Computing and Internet of Things (ICGCIoT) · doi:10.1109/icgciot.2015.7380700 · Cited by 4
“[7] Thus for multiple data sets it is promising to first anonymize all the data sets and then encrypt them before storing or sharing in to clouds.”
BackgroundA review of privacy preserving models for multi-party data release framework
Sharma, S. G., & Rajawat, A. S. (2016) · doi:10.1145/2909067.2909098 · Cited by 4
Scalable, Efficient Anonymization with INCOGNITO - Framework & Algorithm
Chakravorty, A., Rong, C., Jayaram, K. R., et al. (2017) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2017.15 · Cited by 4
“In the anonymized table, three ECs were created having records with IDs {EC1 = [4, 6, 9], EC2 = [1, 5, 9], EC3 = [2, 8, 3]}.”
BackgroundMethodThe Conflict Between Big Data and Individual Privacy
Xu, L., Jiang, C., Qian, Y., & Ren, Y. (2018) · Springer eBooks · doi:10.1007/978-3-319-77965-2_1 · Cited by 3
Cuzzocrea, A., & Carlo, M. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8621882 · Cited by 4
“, [36]) is a generalization method that consists in making published data as much as possible hostile so that knowledge expressed by such data is still useful while”
MethodA Study on Models and Techniques of Anonymization in Data Publishing
Sharma, S., Choudhary, N., & Jain, K. (2019) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset19629 · Cited by 4
Su, J., Cao, Y., & Chen, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26763-6_38 · Cited by 4
Dodero, J. M., Rodríguez-García, M., Ruiz‐Rube, I., & Palomo‐Duarte, M. (2019) · Journal of Web Engineering · doi:10.13052/jwe1540-9589.1875 · Cited by 4
“…code-level interventions to improve an application’s security and privacy are inspired by and framed in the Security by Design (SbD) and PbD principles, which have significant expressions in Role-Based Access Control (RBAC) and Privacy-Preserving Data Publishing (PPDP), among other techniques [15].”
BackgroundMethodSurvey on Privacy Preservation Technique
Shimona, S. (2020) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict48043.2020.9112584 · Cited by 4
“– Egg: when you choose a person from an escape his records should meet some of necessary requirement and this method is referred as k-anonymity [16]. a) Key Attribute -”
BackgroundSecurity Analysis for Machine Learning and Image Processing Related Information Systems
Sharma, R., & Davuluri, K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51859-2_13 · Cited by 4
Scalable Two-Phase Top-Down Specification for Big Data Anonymization Using Apache Pig
Raj, A., & D’Souza, R. G. L. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-3514-7_75 · Cited by 4
A Novel Method for Privacy Preservation of Health Data Stream
Puri, G. D. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/110942020 · Cited by 4
Deep Poisoning: Towards Robust Image Data Sharing against Visual Disclosure
Guo, H., Dolhansky, B., Hsin, E., Dinh, P., Ferrer, C. C., & Wang, S. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00073 · Cited by 4
“To protect information privacy, privacy-preserving data publishing (PPDP) [6, 56] has been studied for a long time.”
BackgroundEmbracing Differential Privacy for Anonymizing Spontaneous ADE Reporting Data
Lin, W.-Y., & Shen, Z.-X. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313578 · Cited by 4
“…Salary, etc., and Non-sensitive Attributes ( NSA ) that not fall into the above three categories Since Sweeney [21] pointed out that publishing microdata by only removing ID without paying attention to QID may threat the privacy of data owners, there have been a lot of research on this topic [13].”
BackgroundDifferentially Private Decision Tree Based on Pearson’s Correlation Coefficient
Yan, Q., Wang, J., Liu, S., et al. (2021) · International Conference on Information Science and Technology · doi:10.1109/icist52614.2021.9440604 · Cited by 4
“Many privacy preserving algorithms have been proposed, but only few have considered di ff erentially private classifi-cation algorithms [31].”
VERİ MAHREMİYETİ: SALDIRILAR, KORUNMA VE YENİ BİR ÇÖZÜM ÖNERİSİ
Vural, Y. (2018) · ULUSLARARASI BİLGİ GÜVENLİĞİ MÜHENDİSLİĞİ DERGİSİ · doi:10.18640/ubgmd.664416 · Cited by 4
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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