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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Clifton et al. describe a toolkit for various privacy-preserving SMC-based data mining operations including secure sum computation using a secret random number.
“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 this protocol, two adjacent parties can collude to compute the random number.
“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]
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Shaneck, M., Kim, Y., & Kumar, V. (2006) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2006.133 · Cited by 66 · see citing works
Privacy-aware regression modeling of participatory sensing data
Ahmadi, H., Pham, N., Ganti, R., Abdelzaher, T., Nath, S., & Han, J. (2010) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1869983.1869994 · Cited by 66 · see citing works
Agrawal, R., Asonov, D., Kantarcıoğlu, M., & Li, Y. (2006) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2006.144 · Cited by 66 · see citing works
Efficient, Differentially Private Point EstimatorsPreprint
Smith, A. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0809.4794 · Cited by 66 · see citing works
A Robust Sampling-Based Framework for Privacy Preserving OLAP
Cuzzocrea, A., Russo, V., & Saccà, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85836-2_10 · Cited by 64 · see citing works
Privacy-preserving back-propagation and extreme learning machine algorithms
Samet, S., & Miri, A. (2012) · Data & Knowledge Engineering · doi:10.1016/j.datak.2012.06.001 · Cited by 64 · see citing works
Secure Two-Party Differentially Private Data Release for Vertically Partitioned Data
Mohammed, N., Alhadidi, D., Fung, B. C. M., & Debbabi, M. (2013) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2013.22 · Cited by 64 · see citing works
Secure multiparty aggregation with differential privacy
Goryczka, S., Xiong, L., & Sunderam, V. S. (2013) · EDBT/ICDT Workshops · doi:10.1145/2457317.2457343 · Cited by 64 · see citing works
Cheng, P.-H., Huang, T.-H., Chien, Y.-W., Wu, C.-L., Tai, C.-S., & Fu, L. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.105512 · Cited by 64 · see citing works
- 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 › Artificial Intelligence › 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
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.
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