The DOI matches its Crossref record; 18 of the 19 references it cites were found in a registry or an index.
Tools for privacy preserving distributed data mining
Abstract
Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacy-preserving data mining problems.
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In the citing paper
“Clifton et al. [117] describe a toolkit for various privacypreserving SMC-based data mining operations including secure sum computation using a secret random number.”
From A Survey of Self-Sovereign Identity Ecosystem (2021) · cites this paper as [117]
In the citing paper
“However, in this protocol, two adjacent parties can collude to compute the random number.”
From A Survey of Self-Sovereign Identity Ecosystem (2021) · cites this paper as [117]
Citations
Cites
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,362
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,454
Goldreich, O., Micali, S., & Wigderson, A. (1987) · Proceedings of the nineteenth annual ACM conference on Theory of computing - STOC '87 · doi:10.1145/28395.28420 · Cited by 3,590
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 3,009
How to generate and exchange secrets
Yao, A. (1986) · 27th Annual Symposium on Foundations of Computer Science (sfcs 1986) · doi:10.1109/sfcs.1986.25 · Cited by 2,281
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335438 · Cited by 1,719
Pohlig, S., & Hellman, M. (1978) · IEEE Transactions on Information Theory · doi:10.1109/tit.1978.1055817 · Cited by 1,199
Privacy Preserving Data Mining
Lindell, Y., & Pinkas, B. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44598-6_3 · Cited by 1,077
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
Self-citationPrivacy-preserving distributed mining of association rules on horizontally partitioned data
Clifton, C., Kantarcioglu, M., Clifton, C., & Member, S. (2010) · Cited by 888
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Research works printed but not linked: 7
- 10.5555/645920.672836 · DOI
- 10.5555/645933.756665 · DOI
- 10.5555/382006.383194 · DOI
- A. Eisenberg . With false numbers, data crunchers try to mine the truth . New York Times , July 18 2002 .]] A. Eisenberg. With false numbers, data crunchers try to mine the truth. New York Times, July 18 2002.]]
- M. Hamblen . Privacy algorithms : Technology-based protections could make personal data impersonal . Computerworld , Oct. 14 2002 .]] M. Hamblen. Privacy algorithms: Technology-based protections could make personal data impersonal. Computerworld, Oct. 14 2002.]]
- 10.5555/850943.853036 · DOI
- B. Schneier. Applied Cryptography. John Wiley & Sons , 2 nd edition, 1995 .]] B. Schneier. Applied Cryptography. John Wiley & Sons, 2nd edition, 1995.]]
Cited by
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Items 801–885 of 885
Clear, M., Patsakis, C., & Laird, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.7118
Fuzzy Framework for Preserving Privacy in Neural Networks Classification
Bashir, M., Asger, M., Ali, R., & Arif, T. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016910290
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Tran, D. H., Ng, W. K., Wong, Y. D., & Vinh Van, T. H. A. I. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31863-9_7
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Chawla, P., Bhatnagar, R., & Han, C. Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-40349-6_22
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Xu, S., Qian, Y., & Hu, R. Q. (2016) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2016.7568482
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da Silva, J. C., Klusch, M., & Lodi, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46349-0_23
Pathak, F. A. N., & Pandey, S. B. S. (2013) · Nirma University International Conference on Engineering · doi:10.1109/nuicone.2013.6780073
Survey of Secure Two Parties Confidential Information Release in Vertical Partitioned Data
Rajkumar, M., Vijayalakshmi, A., & Shiny, M. U. (2016) · World Scientific News
A Novel and Privacy Preserving Unsupervised Learning Approach between Data Holders
Srilatha, P., & Chadrashekar, M. V. (2015)
Privacy Preservation for Cloud-Based Data Sharing and Data Analytics
Zheng, Y. R. (2016) · VTechWorks (Virginia Tech)
Privacy Preserving Fuzzy Based Decision Tree Classifier
Amma, N. G. N., & Dhanaseelan, F. R. (2017) · International Journal of Advanced Research in Computer Science and Software Engineering · doi:10.23956/ijarcsse/v6i12/0200
Privacy Preserving Sequential Pattern Mining in Distributed Databases
Kapoor, V., Subhas, N., Hind, A., & Marg, F. (2008)
An Algorithm Of Anonymous Id Assignment For Secure Data Sharing On A Network
Harish, M. S., Ramamurthy, A., & Subbarao, K. T. V. (2014)
DATA MINING IN DATABASES STORED OVER THE INTERNET
Mapako, B., & Abia, M. (2013)
Contributions to secure multicast and privacy-preserving computations on peer-to-peer networks
Naranjo, J. A. M. (2013) · Dialnet (Universidad de la Rioja)
Perspective Challenges & Proposed Solution for Ppdm Using Data Mining Techniques
Sharma, C. (2017) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2017.8298
Evaluating Parallel Minibatch Training for Machine Learning Applications
Dreiseitl, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-74718-7_48
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Shah, A. K., & Gulati, R. M. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-73712-6_3
Nagaratna, M., & Sowmya, Y. (2017) · International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC) · doi:10.1109/iccpeic.2017.8290334
Abduction and Anonymity in Data Mining
Atzori, M. (2006) · Electronic Theses and Dissertations Repository (University of Pisa)
Private Record Matching For Unstructured Data Using Hybrid Approach
(2017) · i-manager’s Journal on Software Engineering · doi:10.26634/jse.11.4.13818
Data analytic framework for crime sector using open grid rule generation algorithm
Begum, M. S., & George, A. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i1.9.9811
Unifi: A Protocol For Association Rule Mining Invertically Distributed Databases
Babu, M. S. (2015) · i-manager s Journal on Computer Science · doi:10.26634/jcom.3.2.3544
Caring without sharing: Meta-analysis 2.0 for massive genome-wide association studiesPreprint
Pourshafeie, A., Bustamante, C. D., & Prabhu, S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/436766
A Privacy Protection Method for Learning Artificial Neural Network on Vertically Distributed Data
Li, G., Su, X., & Wang, Y. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-00214-5_142
Privacy-Preserving Probabilistic Forecasting for Temporal-spatial Correlated Wind FarmsPreprint
Jia, M., Shen, C., Wang, Z., & Yu, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.01126
Data safekeeping against knowledge discovery
Raś, Z. W., & Im, S. (2006)
Private-Preserving Analysis of Network Traffic
Tang, Y. S., & Ma, Y. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-7123-3_5
Preserving privacy in data publishing and analysis.
Li, Y. (2011) · Adelaide Research & Scholarship (AR&S) (University of Adelaide)
A Sensitive Attribute based Clustering Method for kanonymizationPreprint
Bhaladhare, P., & Jinwala, D. C. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1203.3622
Approach to Privacy Preserved Data Mining in Distributed Systems
Anikin, I. V., & Gazimov, R. M. (2019) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon.2019.8867812
Contributions to the Formalization and Extraction of Generic Bases of Association RulesPreprint
Yahia, S. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00524
A Game-based Method for User Privacy Protection in Big Data
Li, X., Chen, C., Liu, F., Li, B., & Wang, Y. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/719/1/012011
Systems Support for Emerging Memory Technologies
Sartakov, V. A. (2020) · Digitale Bibliothek Braunschweig (Verbundzentrale Göttingen (VZG)) · doi:10.24355/dbbs.084-202001281133-0
Lu, Y., & Zhang, Y. (2020) · Emerging topics in statistics and biostatistics · doi:10.1007/978-3-030-33416-1_8
Distributed Privacy-Preserving Iterative Summation Protocols
Liu, Y., Liu, Q., Zhang, X., Qin, S., & Lei, X. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.3006081
Backtracking Algorithms for Constructing the Hamiltonian Decomposition of a 4-regular Multigraph
Korostil, A. V., & Nikolaev, A. V. (2021) · Modeling and Analysis of Information Systems · doi:10.18255/1818-1015-2021-1-6-21
Matrix Decomposition Techniques for Data Privacy
Zhang, J., Wang, J., & Xu, S. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-010-3.ch185
Kavitha, P. (2017) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit1725100
A Protocol for Checking Equality of Data Using Hash in Ideal Model of Secure Multiparty Computation
Sheikh, R., Sheikh, R., Mishra, D. K., & Dubey, M. (2021) · Journal of University of Shanghai for Science and Technology · doi:10.51201/jusst12654
Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering
Li, N., Huang, L., Li, Y., & Sun, M. (2021) · Computer and Information Science · doi:10.5539/cis.v14n2p26
Aida: Efficient Algorithm For Anonymous Sharing Of Private Data In Distributed Networks
Bansod, A. (2018) · JournalNX
Secure Determining of the k-th Greatest Element Among Distributed Private Values
Jaberi, M., & Mala, H. (2021) · International Computer Conference, Computer Society of Iran (CSICC) · doi:10.1109/csicc52343.2021.9420567
Decentralized online integer programming problems with a coupling cardinality constraint
Karabulut, E., Ahmed, S., & Nemhauser, G. L. (2021) · Computers & Operations Research · doi:10.1016/j.cor.2021.105421
A Systematic Approach Used in Privacy Preservation for Collaborative Data Publishing
Akhand, P., Hiwarkar, T., Bhandekar, R., Suke, A. P., Ingole, M., & Saha, R. (2021) · International Journal of Innovations in Engineering and Science · doi:10.46335/ijies.2021.6.8.7
A Study on Efficient and Secure Set Similarity Joins
Mateus, S. (2018) · Terrestrial Environment Research Center (University of Tsukuba)
Kumar, V. S. (2017) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v5i8.1170
Multiple-sized Bucketization For Privacy Protection
Wang, P. (2014) · Summit (Simon Fraser University)
An efficient secure sum of multi-scalar products protocol base on elliptic curve
Van, V. T., Dung, L. T., Quan, H. V., & Lượng, T. T. (2022) · Journal of Science and Technology on Information security · doi:10.54654/isj.v14i2.196
Ye, F., Qian, Y., & Hu, R. Q. (2018) · Wiley eBooks · doi:10.1002/9781119240136.refs
The Computation of Private Set Intersection Cardinality over Complex Number
唐, 桐. (2021) · Computer Science and Application · doi:10.12677/csa.2021.116170
Cryptographic protocol for finding the meeting place of participants with confidentiality property
Ioganson, I. D., Golovanov, A. A., Dakuo, J.-M. N., & Davydov, V. V. (2022) · The Herald of the Siberian State University of Telecommunications and Information Science · doi:10.55648/1998-6920-2022-16-2-33-39
Nguyen, V. C. (2022) · Journal of Science and Technique · doi:10.56651/lqdtu.jst.v11.n01.362.ict
Privacy Preserving of Two Local Data Sites Using Global Random Forest Classification
Gadepaka, L., & Bapi, R. S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-15784-4_10
Privacy-preserving personal data analysis
Sun, C. (2022) · doi:10.26481/dis.20221114cs
Backtracking Algorithms for Constructing the Hamiltonian Decomposition of a 4-Regular Multigraph
Korostil, A. V., & Nikolaev, A. V. (2022) · Automatic Control and Computer Sciences · doi:10.3103/s0146411622070082
Việt, T. Q., Anh, C. T., & Bắc, L. H. (2014) · Journal on Information Technologies & Communications · doi:10.32913/mic-ict-research-vn.v1.n27.106
A Comparison of Methods for Neural Network Aggregation
Segev, J. P. A. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1160
Efficient Gossip Algorithms for Machine Learning
Danner, G. (2022) · doi:10.14232/phd.11135
Modified Algorithm for Privacy Preservation of Gathered Data
Sharma, N. N. S. (2023) · Journal of Propulsion Technology · doi:10.52783/tjjpt.v44.i4.2560
Finding a second Hamiltonian decomposition of a 4-regular multigraph by integer linear programming
Nikolaev, A. V., & Klimov, E. V. (2024) · Journal of Combinatorial Optimization · doi:10.1007/s10878-024-01184-0
Computational Geometry Based on Quantum Secure Multi-Party Summation and Multiplication
Tang, Y., Liu, J., Wang, Y., & Yu, J. (2025) · International Journal of Theoretical Physics · doi:10.1007/s10773-025-06138-8
Research on privacy-preserving distributed data mining methods
Ziying, L. (2025) · International Conference on Machine Learning, Neural Networks, and Computer Software (MLNNCS) · doi:10.1117/12.3090722
Association Mining Algorithm for Privacy Preserving on Horizontally Distributed Datasets
Lv, J. (2009) · Jisuanji gongcheng
Privacy-Preserving Bayesian Network Learning From Heterogeneous Distributed Data.
Ma, J., & Sivakumar, K. (2006)
CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION
Thapa, N. (2013) · UKnowledge (University of Kentucky)
Information-Theoretic Secure Privacy-Preserving Average Computation Scheme
Zhang, Y., Liu, X., Liang, L., Zhang, Y., Ye, W., & Gu, Y. (2026) · Mechanisms and machine science · doi:10.1007/978-3-032-01363-7_35
Discouraging abusive behavior in privacy-preserving decentralized online social networks
García-Recuero, Á. (2017) · theses.fr (ABES) · doi:10.70675/498740f2z5e14z4d2fz936dz2577e5d5b173
Meng, X. (2026) · Big data management · doi:10.1007/978-981-95-5787-5_2
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Brunn, C., & Tschorsch, F. (2026) · ACM Sustainability Week · doi:10.1145/3765611.3815143
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Vatsalan, D., Asghar, H., & Kâafar, M. A. (2026) · Advances in information security · doi:10.1007/978-3-032-21932-9_7
Vatsalan, D., Asghar, H., & Kâafar, M. A. (2026) · Advances in information security · doi:10.1007/978-3-032-21932-9_9
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Vatsalan, D., Asghar, H., & Kâafar, M. A. (2026) · Advances in information security · doi:10.1007/978-3-032-21932-9_4
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Zielinski, M. P. (2010) · UpSpace Institutional Repository (University of Pretoria)
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Metadata
- Authors
- Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y.
- Affiliations
- Purdue University West Lafayette (US)
- Purdue University, West Lafayette, IN
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2002-12
- Volume
- 4
- Issue
- 2
- Pages
- 28-34
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2002352982
- Cited by
- 890
- References
- 19
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 12 · 2023: 14 · 2022: 27 · 2021: 38 · 2020: 31 · 2019: 40 · 2018: 46 · 2017: 41 · 2016: 51 · 2015: 57 · 2014: 70 · 2013: 68 · 2012: 50
- FWCI
- 14.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Explorations Newsletter (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving data mining, distributed data mining, privacy definitions, collaborative computing, privacy-preserving association rule mining, privacy-preserving clustering
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