A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment

Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

A Review of Data Security Primitives in Data Mining

This paper has discussed various issues and security primitives like Spatial Data Handing, Privacy Protection of data, Data Load Balancing, Resource Mining etc. in the area of Data Mining.A 5-stage review process has been conductedfor 30 research papers which were published in the period of year ranging from 1996 to year 2013. After an exhaustive review process, nine key issues were found “Spatial Data Handing, Data Load Balancing, Resource Mining ,Visual Data Mining, Data Clusters Mining, Privacy Preservation, Mining of gaps between business tools & patterns, Mining of hidden complex patterns.” which have been resolved and explained with proper methodologies. Several solution approaches have been discussed in the 30 papers. This paper provides an outcome of the review which is in the form of various findings, found under various key issues. The findings included algorithms and methodologies used by researchers along with their strengths and weaknesses and the scope for the future work in the area

Literature Review on Secure Mining of Association Rules in Horizontally Distributed Databases

Data and knowledge Engineering is one of the area under data mining. Which can extract important knowledge from large database, but sometimes these database are divided among various parties. This paper addresses a fast distributed mining of association rules over horizontally distributed data. This paper presents different methods for secure mining of association rules in horizontally distributed databases. The main aim of this paper is protocol for secure mining of association rules in horizontally distributed databases. The current main protocol is that of Kantarcioglu and Clifton. This protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al., which is an unsecured distributed version of the Apriori algorithm. The main components in this protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. This protocol offers improved privacy with respect to the protocol in. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost