Privacy preserving association rule mining in vertically partitioned data
Abstract
Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association rules. However, the sites must not reveal individual transaction data. We present a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.
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The secret sharing approach offers no accuracy loss and computation much faster than HE.
“By contrast, the secret sharing approach offers the following advantages: (i) there is no accuracy loss, (ii) computation is much faster than HE approach.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [21]
The major drawback of the secret sharing approach is that one has to offline generate and store many triplets before online computation.
“The major drawback of the secret sharing approach is that one has to offline generate and store many triplets before online computation.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [21]
The HE approach is simple but computationally expensive.
“The HE approach is simple but computationally expensive.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [21]
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- Authors
- Vaidya, J., & Clifton, C.
- Affiliations
- Purdue University West Lafayette (US)
- Venue
- Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2002-07-23
- Pages
- 639-644
- OpenAlex
- W2093367651
- Cited by
- 1,001
- References
- 24
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 17 · 2023: 25 · 2022: 22 · 2021: 25 · 2020: 41 · 2019: 28 · 2018: 38 · 2017: 40 · 2016: 40 · 2015: 45 · 2014: 60 · 2013: 69 · 2012: 51
- FWCI
- 33.09
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- privacy-preserving data mining, association rule mining, vertical partitioning, frequent itemset mining, two-party computation, distributed transactions, secure multi-party computation, transaction privacy
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