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
Mobile Crowd Sensing and Computing
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015) · ACM Computing Surveys · doi:10.1145/2794400 · Cited by 723
“Therefore, new privacy-preserving approaches need to be developed to ensure that important information cannot be gleaned from mined patterns [Matatov et al. 2010; Fung et al. 2010; Christin et al. 2011].”
BackgroundAI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
“PPDP objective is to preserve patients’ privacy while achieving the minimum possible loss of information [53].”
IoT Middleware: A Survey on Issues and Enabling technologies
Ngu, A. H. H., Gutiérrez, M., Metsis, V., Nepal, ., & Sheng, Q. Z. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2615180 · Cited by 685
“We refer readers to [58]–[60] for further information.”
BackgroundYin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638
Information Security in Big Data: Privacy and Data Mining
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. S. (2014) · IEEE Access · doi:10.1109/access.2014.2362522 · Cited by 648
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
