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

Privacy Preserving ID3 over Horizontally, Vertically and Grid Partitioned Data

We consider privacy preserving decision tree induction via ID3 in the case where the training data is horizontally or vertically distributed. Furthermore, we consider the same problem in the case where the data is both horizontally and vertically distributed, a situation we refer to as grid partitioned data. We give an algorithm for privacy preserving ID3 over horizontally partitioned data involving more than two parties. For grid partitioned data, we discuss two different evaluation methods for preserving privacy ID3, namely, first merging horizontally and developing vertically or first merging vertically and next developing horizontally. Next to introducing privacy preserving data mining over grid-partitioned data, the main contribution of this paper is that we show, by means of a complexity analysis that the former evaluation method is the more efficient.Comment: 25 page

Privacy preserving data mining

A fruitful direction for future data mining research will be the development of technique that incorporates privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We analyze the possibility of privacy in data mining techniques in two phasesrandomization and reconstruction. Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. To preserve client privacy in the data mining process, techniques based on random perturbation of data records are used. Suppose there are many clients, each having some personal information, and one server, which is interested only in aggregate, statistically significant, properties of this information. The clients can protect privacy of their data by perturbing it with a randomization algorithm and then submitting the randomized version. This approach is called randomization. The randomization algorithm is chosen so that aggregate properties of the data can be recovered with sufficient precision, while individual entries are significantly distorted. For the concept of using value distortion to protect privacy to be useful, we need to be able to reconstruct the original data distribution so that data mining techniques can be effectively utilized to yield the required statistics. Analysis Let xi be the original instance of data at client i. We introduce a random shift yi using randomization technique explained below. The server runs the reconstruction algorithm (also explained below) on the perturbed value zi = xi + yi to get an approximate of the original data distribution suitable for data mining applications. Randomization We have used the following randomizing operator for data perturbation: Given x, let R(x) be x+€ (mod 1001) where € is chosen uniformly at random in {-100…100}. Reconstruction of discrete data set P(X=x) = f X (x) ----Given P(Y=y) = F y (y) ---Given P (Z=z) = f Z (z) ---Given f (X/Z) = P(X=x | Z=z) = P(X=x, Z=z)/P (Z=z) = P(X=x, X+Y=Z)/ f Z (z) = P(X=x, Y=Z - X)/ f Z (z) = P(X=x)*P(Y=Z-X)/ f Z (z) = P(X=x)*P(Y=y)/ f Z (z) Results In this project we have done two aspects of privacy preserving data mining. The first phase involves perturbing the original data set using ‘randomization operator’ techniques and the second phase deals with reconstructing the randomized data set using the proposed algorithm to get an approximate of the original data set. The performance metrics like percentage deviation, accuracy and privacy breaches were calculated. In this project we studied the technical feasibility of realizing privacy preserving data mining. The basic promise was that the sensitive values in a user’s record will be perturbed using a randomizing function and an approximate of the perturbed data set be recovered using reconstruction algorithm

Privacy Preserving Data Mining For Horizontally Distributed Medical Data Analysis

To build reliable prediction models and identify useful patterns, assembling data sets from databases maintained by different sources such as hospitals becomes increasingly common; however, it might divulge sensitive information about individuals and thus leads to increased concerns about privacy, which in turn prevents different parties from sharing information. Privacy Preserving Distributed Data Mining (PPDDM) provides a means to address this issue without accessing actual data values to avoid the disclosure of information beyond the final result. In recent years, a number of state-of-the-art PPDDM approaches have been developed, most of which are based on Secure Multiparty Computation (SMC). SMC requires expensive communication cost and sophisticated secure computation. Besides, the mining progress is inevitable to slow down due to the increasing volume of the aggregated data. In this work, a new framework named Privacy-Aware Non-linear SVM (PAN-SVM) is proposed to build a PPDDM model from multiple data sources. PAN-SVM employs the Secure Sum Protocol to protect privacy at the bottom layer, and reduces the complex communication and computation via Nystrom matrix approximation and Eigen decomposition methods at the medium layer. The top layer of PAN-SVM speeds up the whole algorithm for large scale datasets. Based on the proposed framework of PAN-SVM, a Privacy Preserving Multi-class Classifier is built, and the experimental results on several benchmark datasets and microarray datasets show its abilities to improve classification accuracy compared with a regular SVM. In addition, two Privacy Preserving Feature Selection methods are also proposed based on PAN-SVM, and tested by using benchmark data and real world data. PAN-SVM does not depend on a trusted third party; all participants collaborate equally. Many experimental results show that PAN-SVM can not only effectively solve the problem of collaborative privacy-preserving data mining by building non-linear classification rules, but also significantly improve the performance of built classifiers