Secure Two-Party Association Rule Mining Based on One-Pass FP-Tree

2011 ◽  
Vol 5 (2) ◽  
pp. 13-32 ◽  
Author(s):  
Golam Kaosar ◽  
Xun Yi
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Secure Computation ◽  
Rule Mining ◽  
Fully Homomorphic Encryption ◽  
Popular Method ◽  
Database Server ◽  
Tree Method

Frequent Path tree (FP-tree) is a popular method to compute association rules and is faster than Apriori-based solutions in some cases. Association rule mining using FP-tree method cannot ensure entire privacy since frequency of the itemsets are required to share among participants at the first stage. Moreover, FP-tree method requires two scans of database transactions which may not be the best solution if the database is very large or the database server does not allow multiple scans. In addition, one-pass FP-tree can accommodate continuous or periodically changing databases without restarting the process as opposed to a regular FP-tree based solution. In this paper, the authors propose a one-pass FP-tree method to perform association rule mining without compromising any data privacy among two parties. A fully homomorphic encryption system over integer numbers is applied to ensure secure computation among two data sites without disclosing any number belongs to themselves.

Secure Two-Party Association Rule Mining Based on One-Pass FP-Tree

2013 ◽  
pp. 82-102
Author(s):  
Golam Kaosar ◽  
Xun Yi
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Secure Computation ◽  
Rule Mining ◽  
Fully Homomorphic Encryption ◽  
Popular Method ◽  
Tree Method

Frequent Path tree (FP-tree) is a popular method to compute association rules and is faster than Apriori-based solutions in some cases. Association rule mining using FP-tree method cannot ensure entire privacy since frequency of the itemsets are required to share among participants at the first stage. Moreover, FP-tree method requires two scans of database transactions which may not be the best solution if the database is very large or the database server does not allow multiple scans. In addition, one-pass FP-tree can accommodate continuous or periodically changing databases without restarting the process as opposed to a regular FP-tree based solution. In this paper, the authors propose a one-pass FP-tree method to perform association rule mining without compromising any data privacy among two parties. A fully homomorphic encryption system over integer numbers is applied to ensure secure computation among two data sites without disclosing any number belongs to themselves.

Analysis and Evaluation of Novel Privacy Preserving Techniques for Collaborative Temporal Association Rule Mining Using Secret Sharing

2014 ◽  
Vol 5 (3) ◽  
pp. 58-76 ◽  
Author(s):  
Nirali R. Nanavati ◽  
Neeraj Sen ◽  
Devesh C. Jinwala
Keyword(s):  
Secret Sharing ◽  
Association Rule ◽  
Association Rule Mining ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Experimental Results ◽  
Public Key ◽  
Secret Sharing Scheme ◽  
Rule Mining ◽  
Sharing Scheme

With digital data being abundant in today's world, competing organizations desire to gain insights about the market, without putting the privacy of their confidential data at risk. This paper provides a new dimension to the problem of Privacy Preserving Distributed Association Rule Mining (PPDARM) by extending it to a distributed temporal setup. It proposes extensions of public key based and non-public key based additively homomorphic techniques, based on efficient private matching and Shamir's secret sharing, to privately decipher these global cycles in cyclic association rules. Along with the theoretical analysis, it presents experimental results to substantiate it. This paper observes that the Secret Sharing scheme is more efficient than the one based on Paillier homomorphic encryption. However, it observes a considerable increase in the overhead associated with the Shamir's secret sharing scheme, as a result of the increase in the number of parties. To reduce this overhead, it extends the secret sharing scheme without mediators to a novel model with a Fully Trusted and a Semi Trusted Third Party. The experimental results establish this functioning for global cycle detections in a temporal setup as a case study. The novel constructions proposed can also be applied to other scenarios that want to undertake Secure Multiparty Computation (SMC) for PPDARM.

Preserving Privacy in Mining Quantitative Associations Rules

2011 ◽  
pp. 310-326
Author(s):  
Madhu V. Ahluwalia ◽  
Aryya Gangopadhyay ◽  
Zhiyuan Chen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
Input Data ◽  
Academic Community ◽  
Discrete Wavelet ◽  
Rule Mining ◽  
Privacy Preserving Data Mining

Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.

scholarly journals Privacy Preserving with Association Rule Mining using Evolutionary Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 11893-11899
Keyword(s):  
Data Mining ◽  
Optimization Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Multiple Data ◽  
Modified Algorithm

Privacy-Preserving-Data-Mining (PPDM) is a novel study which goals to protect the secretive evidence also circumvent the revelation of the evidence through the records reproducing progression. This paper focused on the privacy preserving on vertical separated databases. The designed methodology for the subcontracted databases allows multiple data viewers besides vendors proficiently to their records securely without conceding the secrecy of the data. Privacy Preserving Association Rule-Mining (PPARM) is one method, which objects to pelt sensitivity of the association imperative. A new efficient approach lives the benefit since the strange optimizations algorithms for the delicate association rule hiding. It is required to get leak less information of the raw data. The evaluation of the efficient of the proposed method can be conducting on some experiments on different databases. Based on the above optimization algorithm, the modified algorithm is to optimize the association rules on vertically and horizontally separated database and studied their performance

Preserving Privacy in Mining Quantitative Associations Rules

2009 ◽  
Vol 3 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Madhu V. Ahluwalia ◽  
Aryya Gangopadhyay ◽  
Zhiyuan Chen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
State Of The Art ◽  
Academic Community ◽  
Discrete Wavelet ◽  
Rule Mining ◽  
Important Data

Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.

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