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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 201–300 of 1,001
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Ge, Y.-F., Wang, H., Bertino, E., Zhan, Z., Cao, J., Zhang, Y., & Zhang, J. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3302284 · Cited by 49
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BackgroundAnonymizing user profiles for personalized web search
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Xiao, Q., Reiter, M. K., & Zhang, Y. (2015) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2810103.2813645 · Cited by 46
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Categorised ethical guidelines for large scale mobile HCI
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Empirical Evaluation on Synthetic Data Generation with Generative Adversarial Network
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“Indeed, while there are many tools to facilitate privacy-protection in archiving research data (Fung et al., 2010), there have been few applications that cater to the unique ethical aspects of qualitative data as described above.”
BackgroundSMC: A Practical Schema for Privacy-Preserved Data Sharing over Distributed Data Streams
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“Bucketization was reported by Fung, Wang, Chen, and Yu (2010).”
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Choi, H. S., Chakraborty, S., Charbiwala, Z. M., & Srivastava, M. B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23556-6_6 · Cited by 40
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BackgroundSecure Distributed Framework for Achieving ε-Differential Privacy
Alhadidi, D., Mohammed, N., Fung, B. C. M., & Debbabi, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31680-7_7 · Cited by 41
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“However, previous anonymization operationbased approaches have several limitations: From the perspective of optimization objectives, no existing approach tackles both data privacy and transparency requirements; from the perspective of optimization approaches, most are based on greedy strategy [59] or graph search [17,…”
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van den Hoven, J., Helbing, D., Pedreschi, D., et al. (2012) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2012-01691-2 · Cited by 38
Invisible Hand: A Privacy Preserving Mobile Crowd Sensing Framework Based on Economic Models
Liu, B., Zhou, W., Zhu, T., et al. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2611761 · Cited by 38
“The spatial and temporal attributes of a location trajectory can be considered as powerful quasi-identifiers that can be linked to various other kinds of physical data objects [9], [10].”
BackgroundPrivacy-preserving tabular data publishing: A comprehensive evaluation from web to cloud
Abdelhameed, S. A., Moussa, S. M., & Khalifa, M. E. (2017) · Computers & Security · doi:10.1016/j.cose.2017.09.002 · Cited by 38
The Costs of Privacy in Local Energy Markets
Buchmann, E., Kessler, S., Jochem, P. E. P., & Böhm, K. (2013) · IEEE Conference on Business Informatics · doi:10.1109/cbi.2013.36 · Cited by 37
“There is a broad variety of approaches, see [24] for an overview.”
BackgroundCustomized privacy preserving for inherent data and latent data
He, Z., Cai, Z., Sun, Y., Li, Y., & Cheng, X. (2016) · Personal and Ubiquitous Computing · doi:10.1007/s00779-016-0972-2 · Cited by 36
Privacy and utility of private synthetic data for medical data analyses
Appenzeller, A., Leitner, M., Philipp, P., Krempel, E., & Beyerer, J. (2022) · Applied Sciences · doi:10.3390/app122312320 · Cited by 37
“More sophisticated technologies such as Differential Privacy (DP) provide a statistical measure that quantifies the privacy of the individuals contained in the dataset [4].”
BackgroundPrivacy preservation in the dissemination of location data
Terrovitis, M. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2031331.2031334 · Cited by 35
“Good surveys of the rapidly growing PPDD research literature can be found in [15; 24; 42].”
BackgroundClarkson, M. R., & Schneider, F. B. (2010) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf.2010.10 · Cited by 35
Xi, B. (2020) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1511 · Cited by 45
θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records
Khan, R., Tao, X., Anjum, A., Kanwal, T., Malik, S. U. R., Khan, A., Rehman, W., & Maple, C. R. (2020) · Electronics · doi:10.3390/electronics9050716 · Cited by 36
Towards Privacy-Preserving Speech Data Publishing
Qian, J., Han, F., Hou, J., Zhang, C., Wang, Y., & Li, X. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8486250 · Cited by 38
“have thoroughly studied privacy issues in data of various forms, including relational data, graph data, text, images, and videos ( [4], [5], [11], [16], [18], [23]–[25]).”
BackgroundComment on “Unique in the shopping mall: On the reidentifiability of credit card metadata”
Sánchez, D., Mart́ınez, S., & Domingo‐Ferrer, J. (2016) · Science · doi:10.1126/science.aad9295 · Cited by 36
Accuracy-Constrained Privacy-Preserving Access Control Mechanism for Relational Data
Pervaiz, Z., Aref, W. G., Ghafoor, A., & Prabhu, N. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.71 · Cited by 36
“For the state of the art in k-anonymity techniques and algorithms, we refer the reader to a recent survey paper [3].”
BackgroundMethodInfluentialAn anonymization-based privacy-preserving data collection protocol for digital health data
Andrew, J., Jennifer Eunice, R., & Karthikeyan, J. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1125011 · Cited by 40
“Unsurprisingly, privacy-preserving healthcare data collection has become a recent research focus where a good number of literature exists (26–32).”
BackgroundSurvey on Privacy-Preserving Techniques for Microdata Publication
Carvalho, T., Moniz, N., Faria, P., & Antunes, L. F. (2023) · ACM Computing Surveys · doi:10.1145/3588765 · Cited by 57
“In addition, existing surveys only address the most well-known PPT and privacy risk measures for a certain type of disclosure, typically identity disclosure [14, 42, 44, 79, 102, 154, 239, 240].”
BackgroundData privacy: From transparency to fairness
Wu, C. (2024) · Technology and Society · doi:10.1016/j.techsoc.2024.102457 · Cited by 32
Enforcing Differential Privacy for Shared Collaborative Filtering
Li, J., Yang, J., Zhao, Y. D., Liu, B., Zhou, M., Bi, J., & Wang, Q. (2016) · IEEE Access · doi:10.1109/access.2016.2600258 · Cited by 35
“The former emphasizes data anonymization [8], [18] prior to data publishing to prevent the re-identification of individual records.”
BackgroundLe, T. T., Simmons, W. K., Misaki, M., Bodurka, J., White, B. C., Savitz, J. B., & McKinney, B. A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx298 · Cited by 35
“There has been a great deal of research on privacy-preserving data releasing mechanisms (Chen et al. , 2009; Fung et al. , 2010; Yu and Ji, 2014) after several discussions on the theoretical possibility to identify individuals in GWAS data (Homer et al. , 2008; Wang et al. , 2009).”
BackgroundUtility and Privacy Assessments of Synthetic Data for Regression Tasks
Hittmeir, M., Ekelhart, A., & Mayer, R. L. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005476 · Cited by 35
“The reader may consult [5] for a general overview on privacy-preserving data publishing methods.”
BackgroundKanwal, T., Anjum, A., Malik, S. U. R., Sajjad, H., Khan, A., Manzoor, U., & Asheralieva, A. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102224 · Cited by 35
Privacy aware publication of surveillance video
Saini, M. K., Atrey, P. K., Mehrotra, S., & Kankanhalli, M. (2013) · International Journal of Trust Management in Computing and Communications · doi:10.1504/ijtmcc.2013.052523 · Cited by 34
Privacy Mechanisms and Evaluation Metrics for Synthetic Data Generation: A Systematic Review
Osorio-Marulanda, P. A., Epelde, G., Hernandez, M., Isasa, I., Reyes, N. M., & Beristain, A. (2024) · IEEE Access · doi:10.1109/access.2024.3417608 · Cited by 35
“Fung et al. [12] conducted a survey conceptually covering recent developments in privacy-preserving data publishing.”
BackgroundInfluentialPrivacy-preserving publishing microdata with full functional dependencies
Wang, H., & Liu, R. (2010) · Data & Knowledge Engineering · doi:10.1016/j.datak.2010.11.002 · Cited by 34
“We follow the assumption in [10] that the publisher has the expertise to fulfill the data anonymization task.”
BackgroundMethodInfluentialPrivacy preserving big data publishing: a scalable k ‐anonymization approach using MapReduce
Mehta, B. B., & Rao, U. P. (2017) · IET Software · doi:10.1049/iet-sen.2016.0264 · Cited by 34
“An implicit assumption which is not mentioned in any of the existing privacy preserving techniques is with respect to the size of data, which should be small or moderate to achieve desired results [38].”
BackgroundPrivacy of Spatial Trajectories
Chow, C.-Y., & Mokbel, M. F. (2011) · Springer eBooks · doi:10.1007/978-1-4614-1629-6_4 · Cited by 34
Privacy-preserving data publishing: an information-driven distributed genetic algorithm
Ge, Y.-F., Wang, H., Cao, J., Zhang, Y., & Jiang, X. (2024) · World Wide Web · doi:10.1007/s11280-024-01241-y · Cited by 33
Anonymizing Transaction Data to Eliminate Sensitive Inferences
Loukides, G., Gkoulalas-Divanis, A., & Shao, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15364-8_34 · Cited by 33
“There are several principles and algorithms for anonymizing relational data (see [ 3 ] for a survey), but, as shown in [10], they are inadequate for anonymizing transactions without incurring an excessive amount of information loss.”
BackgroundPrivacy-Preserving Frequent Pattern Mining from Big Uncertain Data
Leung, C. K.-S., Hoi, C. S. H., Pazdor, A. G. M., Wodi, B. H., & Cuzzocrea, A. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622260 · Cited by 33
“An alternative model to PPDM is privacy-preserving data publishing (PPDP) [23], in which data are published without disclosing the identity of the data subjects.”
BackgroundToward sensitive document release with privacy guarantees
Sánchez, D., & Batet, M. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.12.013 · Cited by 33
“In addition to data protection methods, the computer science community has proposed formal privacy models [5], within the area of Privacy-Preserving Data Publishing (PPDP) [6] and Data Mining (PPDM) [7, 8].”
MethodEnhancing Sensitivity Classification with Semantic Features Using Word Embeddings
McDonald, G., Macdonald, C., & Ounis, I. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56608-5_35 · Cited by 35
“preserving the privacy of personal data [6, 7].”
SoK: Differentially Private Publication of Trajectory Data
Miranda-Pascual, À., Guerra-Balboa, P., Parra‐Arnau, J., Forné, J., & Strufe, T. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0065 · Cited by 33
“[45], adapted by [62] (adding group linkage attacks), with our extension of reconstruction and prediction attacks.”
BackgroundInfluentialSoK: Privacy-Preserving Data Synthesis
Hu, Y., Wu, F., Li, Q., Long, Y., Garrido, G. M., Ge, C., Ding, B., Forsyth, D. A., Li, B., & Song, D. X. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00002 · Cited by 55
Zhang, X., Liu, C., Nepal, ., Yang, C., Dou, W., & Chen, J. (2013) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2013.235 · Cited by 32
“Data anonymization, extensively studied and widely adopted [4], is an effective way for data privacy preservation.”
BackgroundInfluentialPrivacy-preserving data cube for electronic medical records: An experimental evaluation
Kim, S.-H., Lee, H., & Chung, Y. D. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.09.008 · Cited by 31
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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