A novel privacy-preserving scheme for collaborative frequent itemset mining across vertically partitioned data

2015 ◽  
Vol 8 (18) ◽  
pp. 4407-4420 ◽  
Author(s):  
Nirali R. Nanavati ◽  
Devesh C. Jinwala
Keyword(s):  
Privacy Preserving ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Partitioned Data ◽  
Vertically Partitioned Data

scholarly journals Analysis and Evaluation of Schemes for Secure Sum in Collaborative Frequent Itemset Mining across Horizontally Partitioned Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Nirali R. Nanavati ◽  
Prakash Lalwani ◽  
Devesh C. Jinwala
Keyword(s):  
Privacy Preservation ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Research Direction ◽  
Frequent Itemset ◽  
Public Key ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Partitioned Data ◽  
Real Market

Privacy preservation while undertaking collaborative distributed frequent itemset mining (PPDFIM) is an important research direction. The current state of the art for privacy preservation in distributed frequent itemset mining for secure sum in a horizontally partitioned data model comprises primarily public key based homomorphic schemes which are expensive in terms of the communication and computation cost. The nonpublic key based existing state-of-the-art scheme by Clifton et al. used for secure sum in PPDFIM is efficient but prone to security attacks. In this paper, we propose Shamir’s secret sharing based approaches and a symmetric key based scheme to calculate the secure sum in PPDFIM. These schemes are information theoretically secure under the standard assumptions. We further give a detailed theoretical and empirical evaluation of our proposed schemes for PPDFIM using a real market basket dataset. Our experimental analysis also shows that our schemes perform better in terms of the execution cost compared to the public key based scheme for secure sum in PPDFIM.

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