Survey on Privacy Preserving Association Rule Data Mining

2017 ◽  
Vol 4 (2) ◽  
pp. 63-80 ◽  
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Performance Metrics ◽  
Relevant Information ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Rule Mining ◽  
Research Issues ◽  
Data Mining Techniques

The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

Survey on Privacy Preserving Association Rule Data Mining

2019 ◽  
pp. 624-643
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Performance Metrics ◽  
Relevant Information ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Rule Mining ◽  
Research Issues ◽  
Data Mining Techniques

The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

On Association Rule Mining for the QSAR Problem

2011 ◽  
pp. 83-86
Author(s):  
Luminita Dumitriu
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Neuro Fuzzy ◽  
The 1960S ◽  
New Compounds

The concept of Quantitative Structure-Activity Relationship (QSAR), introduced by Hansch and co-workers in the 1960s, attempts to discover the relationship between the structure and the activity of chemical compounds (SAR), in order to allow the prediction of the activity of new compounds based on knowledge of their chemical structure alone. These predictions can be achieved by quantifying the SAR. Initially, statistical methods have been applied to solve the QSAR problem. For example, pattern recognition techniques facilitate data dimension reduction and transformation techniques from multiple experiments to the underlying patterns of information. Partial least squares (PLS) is used for performing the same operations on the target properties. The predictive ability of this method can be tested using cross-validation on the test set of compounds. Later, data mining techniques have been considered for this prediction problem. Among data mining techniques, the most popular ones are based on neural networks (Wang, Durst, Eberhart, Boyd, & Ben-Miled, 2004) or on neuro-fuzzy approaches (Neagu, Benfenati, Gini, Mazzatorta, & Roncaglioni, 2002) or on genetic programming (Langdon, &Barrett, 2004). All these approaches predict the activity of a chemical compound, without being able to explain the predicted value. In order to increase the understanding on the prediction process, descriptive data mining techniques have started to be used related to the QSAR problem. These techniques are based on association rule mining. In this chapter, we describe the use of association rule-based approaches related to the QSAR problem.

Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

2014 ◽  
Vol 23 (05) ◽  
pp. 1450004 ◽  
Author(s):  
Ibrahim S. Alwatban ◽  
Ahmed Z. Emam
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Area ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Comprehensive Survey ◽  
New Research

In recent years, a new research area known as privacy preserving data mining (PPDM) has emerged and captured the attention of many researchers interested in preventing the privacy violations that may occur during data mining. In this paper, we provide a review of studies on PPDM in the context of association rules (PPARM). This paper systematically defines the scope of this survey and determines the PPARM models. The problems of each model are formally described, and we discuss the relevant approaches, techniques and algorithms that have been proposed in the literature. A profile of each model and the accompanying algorithms are provided with a comparison of the PPARM models.

scholarly journals Road Traffic Accidents Analysis Using Data Mining Techniques

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
G. Janani ◽  
N. Ramya Devi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accident ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
Rule Mining ◽  
Data Mining Techniques

Road Traffic Accidents (RTAs) are a major public concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury. Most of the analysis of road accident uses data mining techniques which provide productive results. The analysis of the accident locations can help in identifying certain road accident features that make a road accident to occur frequently in the locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. Data analysis has the capability to identify different reasons behind road accidents. In the existing system, k-means algorithm is applied to group the accident locations into three clusters. Then the association rule mining is used to characterize the locations. Most state of the art traffic management and information systems focus on data analysis and very few have been done in the sense of classification. So, the proposed system uses classification technique to predict the severity of the accident which will bring out the factors behind road accidents that occurred and a predictive model is constructed using fuzzy logic to predict the location wise accident frequency.

scholarly journals Crime Analysis and Prediction Using Data Mining and Machine Learning Techniques

2021 ◽  
Vol 9 (10) ◽  
pp. 1259-1270
Author(s):  
Jasmeet Kaur
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Crime Rates ◽  
Crime Analysis ◽  
Crime Trends ◽  
Outlier Analysis ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
The Government

Abstract: With the increase in crime rates across the world, it has become important for the Government and crime handling agencies to control the situation as it has put every person in distress. This paper is an attempt to systematically analyze and identify the crime trends across the years, the inter-state relations based on crime rates and categories through the data available, which will help in predicting the crime trends in future and will be instrumental for the Government to take informed actions and improve the country’s situation. This paper applies various data mining techniques in order to analyze the crime records in India. The results of analysis have been compared for various algorithms in the domain of Association Rule Mining, Clustering, Outlier Analysis, Regression and Classification. The paper also attempts to predict the future occurrences of crimes using classification and regression algorithms which use data mining techniques . Keywords: Crime Analysis, Data Mining, Association Rule Mining, Clustering, outlier Analysis, Classification, Regression

scholarly journals Privacy preserving association rule hiding using border based approach

2021 ◽  
Vol 23 (2) ◽  
pp. 1137
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Sensitive Information ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Large Databases ◽  
Hidden Correlations ◽  
Rule Set

<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

Recent Advances in Preserving Privacy Data Mining

2013 ◽  
Vol 798-799 ◽  
pp. 541-544
Author(s):  
Gao Ming Yang ◽  
Jing Zhao Li ◽  
Shun Xiang Zhang
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Evaluation Criteria ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Algorithm Performance ◽  
Data Distortion ◽  
Detailed Evaluation

A number of privacy preserving techniques have been proposed recently in data mining. In this paper, we provide a review of the state-of-the-art methods for privacy preserving data mining. and discuss methods for randomization, secure multipart computation, and so on. We also make a classification for the privacy preserving data mining technologies, and analyze some works in this field, such as data distortion method for achieving privacy preserving association rule mining. Detailed evaluation criteria of privacy preserving algorithm were illustrated, which include algorithm performance, data utility, and privacy protection degree. Finally, the development of privacy preserving data mining for further directions is given.

scholarly journals Privacy Preserving with Association Rule Mining using Evolutionary Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 11893-11899
Keyword(s):  
Data Mining ◽  
Optimization Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Multiple Data ◽  
Modified Algorithm

Privacy-Preserving-Data-Mining (PPDM) is a novel study which goals to protect the secretive evidence also circumvent the revelation of the evidence through the records reproducing progression. This paper focused on the privacy preserving on vertical separated databases. The designed methodology for the subcontracted databases allows multiple data viewers besides vendors proficiently to their records securely without conceding the secrecy of the data. Privacy Preserving Association Rule-Mining (PPARM) is one method, which objects to pelt sensitivity of the association imperative. A new efficient approach lives the benefit since the strange optimizations algorithms for the delicate association rule hiding. It is required to get leak less information of the raw data. The evaluation of the efficient of the proposed method can be conducting on some experiments on different databases. Based on the above optimization algorithm, the modified algorithm is to optimize the association rules on vertically and horizontally separated database and studied their performance

Collusion-Free Privacy Preserving Data Mining

2010 ◽  
Vol 6 (4) ◽  
pp. 30-45 ◽  
Author(s):  
M. Rajalakshmi ◽  
T. Purusothaman ◽  
S. Pratheeba
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Sources ◽  
Sensitive Information ◽  
Distributed Data ◽  
Distributed Environment ◽  
Rule Mining ◽  
Privacy Preserving Data Mining

Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

Optimal Inventory Classification using Data Mining Techniques

2016 ◽  
pp. 236-255 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Consumer Behavior Analysis ◽  
Relationship Extraction ◽  
Using Data ◽  
Research Studies

Data mining has long been used in relationship extraction from large amount of data for a wide range of applications such as consumer behavior analysis in marketing. Data mining techniques, such as classification, association rule mining, temporal association rule mining, sequential pattern mining, decision trees, and clustering, have attracted attention of several researchers. Some research studies have also extended the usage of this concept in inventory management to determine the optimal economic order quantity. Yet, not many research studies have considered the application of the data mining approach on inventory classification to predict the most profitable items which is also a significant factor to the manager for optimal inventory control. In this chapter, three different cases for inventory classification based on loss rule is presented. An example is illustrated to validate the results.

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