Privacy-preserving data mining
Abstract
A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Much of the literature assumes a priori knowledge of the set of quasi-identifiers, or seeks to discover them on a case-by-case basis.
“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 [6]
Sensitive data disclosure is a long-standing challenge in protecting anonymity when data is published and/or analysed.
“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 [6]
Fairlean implements several parity-based fairness measures and algorithms for binary classification and regression, as well as a dashboard to visualize disparity in accuracy and parity.
“Fairlean 1 Implements several parity-based fairness measures and algorithms [1,2,6] for binary classiication and regression as well as a dashboard to visualize disparity in accuracy and parity.”
From Fairness in machine learning: A survey (2024) · cites this paper as [6]
Cites
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417 · see citing works
Regularization of Inverse Problems
Engl, H. W., Hanke, M., & Neubauer, A. (1996) · Springer eBooks · doi:10.1007/978-94-009-1740-8 · Cited by 5,359 · see citing works
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 · see citing works
Cryptography and data security
Denning, D. E. (1982) · Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) · Cited by 1,904 · see citing works
Security-control methods for statistical databases: a comparative study
Adam, N. R., & Worthmann, J. C. (1989) · ACM Computing Surveys · doi:10.1145/76894.76895 · Cited by 975 · see citing works
TRUSTe: an online privacy seal program
Benassi, P. (1999) · Communications of the ACM · doi:10.1145/293411.293461 · Cited by 267 · see citing works
Suppression Methodology and Statistical Disclosure Control
Cox, L. H. (1980) · Journal of the American Statistical Association · doi:10.1080/01621459.1980.10477481 · Cited by 260 · see citing works
Denning, D. E., & Denning, P. J. (1979) · ACM Transactions on Database Systems · doi:10.1145/320064.320069 · Cited by 209 · see citing works
Secure statistical databases with random sample queries
Denning, D. E. (1980) · ACM Transactions on Database Systems · doi:10.1145/320613.320616 · Cited by 204 · see citing works
Dobkin, D., Jones, A. K., & Lipton, R. J. (1979) · ACM Transactions on Database Systems · doi:10.1145/320064.320068 · Cited by 203 · see citing works
A data distortion by probability distribution
Liew, C. K., Choi, U. J., & Liew, C. J. (1985) · ACM Transactions on Database Systems · doi:10.1145/3979.4017 · Cited by 202 · see citing works
Statistical Databases: Characteristics, Problems, and some Solutions
Shoshani, A. (1982) · Very Large Data Bases · Cited by 201 · see citing works
Auditing and Inference Control in Statistical Databases
Chin, F. Y. L., & Özsoyoğlu, G. (1982) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1982.236161 · Cited by 189 · see citing works
On the Question of Statistical Confidentiality
Fellegi, I. P. (1972) · Journal of the American Statistical Association · doi:10.1080/01621459.1972.10481199 · Cited by 176 · see citing works
The statistical security of a statistical database
Traub, J. F., Yemini, Y., & Woźniakowski, H. (1984) · ACM Transactions on Database Systems · doi:10.1145/1994.383392 · Cited by 156 · see citing works
Cranor, L. F. (1999) · Communications of the ACM · doi:10.1145/293411.293440 · Cited by 152 · see citing works
Oppliger, R. (1997) · Communications of the ACM · doi:10.1145/253769.253802 · Cited by 138 · see citing works
A security machanism for statistical database
Beck, L. L. (1980) · ACM Transactions on Database Systems · doi:10.1145/320613.320617 · Cited by 135 · see citing works
Practical data-swapping: the first steps
Reiss, S. P. (1984) · ACM Transactions on Database Systems · doi:10.1145/348.349 · Cited by 121 · see citing works
Design of LDV: a multilevel secure relational database management system
Stachour, P., & Thuraisingham, B. M. (1990) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.54719 · Cited by 114 · see citing works
Ackerman, M. S., & Cranor, L. F. (1999) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/632716.632875 · Cited by 102 · see citing works
Barbará, D., & Sullivan, M. (1997) · ACM SIGMOD Record · doi:10.1145/262762.262764 · Cited by 82 · see citing works
Privacy interfaces for information management
Lau, T. A., Etzioni, O., & Weld, D. S. (1999) · Communications of the ACM · doi:10.1145/317665.317680 · Cited by 75 · see citing works
Schweitzer, J. A. (1987) · Elsevier eBooks · doi:10.1016/b978-0-409-90065-1.50006-2 · Cited by 71 · see citing works
Rubin, A. D., & Geer, D. (1998) · Computer · doi:10.1109/2.708448 · Cited by 65 · see citing works
A study on the protection of statistical data bases
Yu, C., & Chin, F. Y. L. (1977) · Proceedings - ACM-SIGMOD International Conference on Management of Data · doi:10.1145/509404.509432 · Cited by 54 · see citing works
Selective partial access to a database
Conway, R., & Strip, D. R. (1976) · ACM Annual Conference · doi:10.1145/800191.805537 · Cited by 45 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322 · see citing works
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · see citing works
<i>L</i> -diversity: Privacy beyond <i>k</i> -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 · see citing works
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 3,432 · see citing works
Robust De-anonymization of Large Sparse Datasets
Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372 · see citing works
SecureML: A System for Scalable Privacy-Preserving Machine Learning
Mohassel, P., & Zhang, Y. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.12 · Cited by 1,936 · see citing works
Privacy-preserving data publishing: A survey of recent developments
Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704 · see citing works
Privacy preserving association rule mining in vertically partitioned data
Vaidya, J., & Clifton, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775142 · Cited by 1,001 · see citing works
Tools for privacy preserving distributed data mining
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/772862.772867 · Cited by 890 · see citing works
Privacy-preserving <i>k</i> -means clustering over vertically partitioned data
Vaidya, J., & Clifton, C. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956776 · Cited by 626 · see citing works
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
Auditing black-box models for indirect influence
Adler, P., Falk, C., Friedler, S. A., Nix, T., Rybeck, G., Scheidegger, C., Smith, B., & Venkatasubramanian, S. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-017-1116-3 · Cited by 280 · see citing works
Privacy-preserving SVM classification
Vaidya, J., Yu, H., & Jiang, X. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0073-7 · Cited by 178 · see citing works
Privacy-Preserving Decision Trees over Vertically Partitioned Data
Vaidya, J., & Clifton, C. (2005) · Lecture Notes in Computer Science · doi:10.1007/11535706_11 · Cited by 144 · see citing works
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works
A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle
Shahriar, S., Allana, S., Hazratifard, S. M., & Dara, R. (2023) · IEEE Access · doi:10.1109/access.2023.3287195 · Cited by 114 · see citing works
InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy
Zhao, L., Ni, L., Hu, S., Chen, Y., Zhou, P., Xiao, F., & Wu, L. (2018) · IEEE INFOCOM 2018 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2018.8486352 · Cited by 108 · see citing works
Discrimination- and privacy-aware patterns
Hajian, S., Domingo-Ferrer, J., Monreale, A., Pedreschi, D., & Giannotti, F. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0393-7 · Cited by 93 · see citing works
Learning about the world through long-term query logs
Richardson, M. (2008) · ACM Transactions on the Web · doi:10.1145/1409220.1409224 · Cited by 77 · see citing works
Differentially private image classification using support vector machine and differential privacy
Senekane, M. (2019) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010029 · Cited by 38 · see citing works
LLM-PBE: Assessing Data Privacy in Large Language Models
Li, Q., Hong, J., Xie, C., Tan, J., Xin, R., Hou, J., Yin, X., Wang, Z., Hendrycks, D., Wang, Z., et al. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3681954.3681994 · Cited by 42 · see citing works
Privacy Perceptions of Custom GPTs by Users and Creators
Ma, R., Maidhof, C., Carrillo, J. C., Lindqvist, J., & Such, J. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713540 · Cited by 8 · see citing works
- Authors
- Agrawal, R., & Srikant, R.
- Affiliations
- IBM Research - Almaden (US)
- Venue
- Proceedings of the 2000 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 2000-05-16
- Pages
- 439-450
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W4248358572
- Cited by
- 1,719
- References
- 27
- Citations by year
- 2026: 10 · 2025: 33 · 2024: 34 · 2023: 39 · 2022: 60 · 2021: 74 · 2020: 63 · 2019: 52 · 2018: 73 · 2017: 62 · 2016: 68 · 2015: 73 · 2014: 99 · 2013: 98 · 2012: 99
- FWCI
- 10.97
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGMOD International Conference on Management of Data (SIGMOD) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- privacy-preserving data mining, decision tree classification, data perturbation, data reconstruction, aggregated data, classification accuracy
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
