Sandip University
Question
Asked 4 February 2015
Is there any article which discussed case study / application of privacy in distributed data mining?
I want to know about real case study of privacy threat cause of association rule mining (Distributed or centralized database).
Popular answers (1)
All Answers (2)
Jerusalem College of Technology
Dear Nikunj,
Here are references for a few popular papers that might be helpful:
Park, B. H., & Kargupta, H. (2002). Distributed data mining: Algorithms, systems, and applications.
Verykios, V. S., Bertino, E., Fovino, I. N., Provenza, L. P., Saygin, Y., & Theodoridis, Y. (2004). State-of-the-art in privacy preserving data mining. ACM Sigmod Record, 33(1), 50-57.
Aggarwal, C. C., & Philip, S. Y. (2008). A general survey of privacy-preserving data mining models and algorithms (pp. 11-52). Springer US.
Kargupta, H., Das, K., & Liu, K. (2007). Multi-party, privacy-preserving distributed data mining using a game theoretic framework. In Knowledge Discovery in Databases: PKDD 2007 (pp. 523-531). Springer Berlin Heidelberg.
Vaidya, J., Clifton, C. W., & Zhu, Y. M. (2006). Privacy preserving data mining (Vol. 19). Springer Science & Business Media.
Best regards,
Yaakov
3 Recommendations
Similar questions and discussions
Related Publications
Modern organizations are geographically distributed. Typically, each site locally stores its Updated day-to-day data. Using centralized data mining to discover useful patterns in such Organizations data isn't always feasible because merging data sets from different sites into a centralized site causes huge network communication costs. It is impossi...
Purpose
The purpose of this paper is to propose a web intrusion detection system (IDS), SensorWebIDS, which applies data mining, anomaly and misuse intrusion detection on web environment.
Design/methodology/approach
SensorWebIDS has three main components: the network sensor for extracting parameters from real‐time network traffic, the log digger f...
The increasing demand to extend data mining technology to data sets inherently distributed among a large number of autonomous and heterogeneous sources over a network with limited bandwidth has motivated the development of several approaches to distributed data mining and knowledge discovery, of which only a few make use of agents. We briefly revie...



















