Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing

In this paper, we propose a privacy preserving distributed clustering protocol for horizontally partitioned data based on a very efficient homomorphic additive secret sharing scheme. The model we use for the protocol is novel in the sense that it utilizes two non-colluding third parties. We provide a brief security analysis of our protocol from information theoretic point of view, which is a stronger security model. We show communication and computation complexity analysis of our protocol along with another protocol previously proposed for the same problem. We also include experimental results for computation and communication overhead of these two protocols. Our protocol not only outperforms the others in execution time and communication overhead on data holders, but also uses a more efficient model for many data mining applications

A Review on: Association Rule Mining Using Privacy for Partitioned Database

Data Analysis techniques that are Association manage mining and Frequent thing set mining are two prominent and broadly utilized for different applications. The conventional framework concentrated independently on vertically parceled database and on a level plane apportioned databases on the premise of this presenting a framework which concentrate on both on a level plane and vertically divided databases cooperatively with protection safeguarding component. Information proprietors need to know the continuous thing sets or affiliation rules from an aggregate information set and unveil or uncover as few data about their crude information as could reasonably be expected to other information proprietors and outsiders. To guarantee information protection a Symmetric Encryption Technique is utilized to show signs of improvement result. Cloud supported successive thing set mining arrangement used to exhibit an affiliation govern mining arrangement. The subsequent arrangements are intended for outsourced databases that permit various information proprietors to proficiently share their information safely without trading off on information protection. Information security is one of the key procedures in outsourcing information to different outside clients. Customarily Fast Distribution Mining calculation was proposed for securing conveyed information. These business locales an issue by secure affiliation governs over parceled information in both even and vertical. A Frequent thing sets calculation and Distributed affiliation administer digging calculation is used for doing above method adequately in divided information, which incorporates administrations of the information in outsourcing process for disseminated databases. This work keeps up or keeps up proficient security over vertical and flat perspective of representation in secure mining applications

Survey on Secure Mining of Association Rules in Vertically Distributed Databases

A distributed database system is a collection of sites connected on a common high bandwidth network. Logically, data belongs to the same system but physically it is spread over the sites of the network, making the distribution invisible to the user. The advantage of this distribution resides in achieving availability, performance, modularity and reliability. In this paper, I have done a survey of papers related to Mining of Association Rules over distributed databases. From this survey, we have come up with a proposed solution to address the problem of secure mining of association rules where transactions are distributed in vertically distributed databases. Each site holds some attributes of each transaction and the sites wish to participate in the identification of globally valid association rules However, the sites should not reveal individual transaction data. The Protocol is based on Apriori Algorithm [2] and MultiParty Algorithm [3] for efficiently discovering frequent item sets with minimum support levels, without either site communicating individual transaction values. DOI: 10.17762/ijritcc2321-8169.15035