Impacts of frequent itemset hiding algorithms on privacy preserving data mining

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2010Includes bibliographical references (leaves: 54-58)Text in English; Abstract: Turkish and Englishx, 69 leavesThe invincible growing of computer capabilities and collection of large amounts of data in recent years, make data mining a popular analysis tool. Association rules (frequent itemsets), classification and clustering are main methods used in data mining research. The first part of this thesis is implementation and comparison of two frequent itemset mining algorithms that work without candidate itemset generation: Matrix Apriori and FP-Growth. Comparison of these algorithms revealed that Matrix Apriori has higher performance with its faster data structure. One of the great challenges of data mining is finding hidden patterns without violating data owners. privacy. Privacy preserving data mining came into prominence as a solution. In the second study of the thesis, Matrix Apriori algorithm is modified and a frequent itemset hiding framework is developed. Four frequent itemset hiding algorithms are proposed such that: i) all versions work without pre-mining so privacy breech caused by the knowledge obtained by finding frequent itemsets is prevented in advance, ii) efficiency is increased since no pre-mining is required, iii) supports are found during hiding process and at the end sanitized dataset and frequent itemsets of this dataset are given as outputs so no post-mining is required, iv) the heuristics use pattern lengths rather than transaction lengths eliminating the possibility of distorting more valuable data

Data Mining Based on Association Rule Privacy Preserving

The security of the large database that contains certain crucial information, it will become a serious issue when sharing data to the network against unauthorized access. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Association analysis is a powerful tool for discovering relationships which are hidden in large database. Association rules hiding algorithms get strong and efficient performance for protecting confidential and crucial data. Data modification and rule hiding is one of the most important approaches for secure data. The objective of the proposed Association rulehiding algorithm for privacy preserving data mining is to hide certain information so that they cannot be discovered through association rule mining algorithm. The main approached of association rule hiding algorithms to hide some generated association rules, by increase or decrease the support or the confidence of the rules. The association rule items whether in Left Hand Side (LHS) or Right Hand Side (RHS) of the generated rule, that cannot be deduced through association rule mining algorithms. The concept of Increase Support of Left Hand Side (ISL) algorithm is decrease the confidence of rule by increase the support value of LHS. It doesnÊt work for both side of rule; it works only for modification of LHS. In Decrease Support of Right Hand Side (DSR) algorithm, confidence of the rule decrease by decrease the support value of RHS. It works for the modification of RHS. We proposed a new algorithm solves the problem of them. That can increase and decrease the support of the LHS and RHS item of the rule correspondingly so that more rule hide less number of modification. The efficiency of the proposed algorithm is compared with ISL algorithms and DSR algorithms using real databases, on the basis of number of rules hide, CPU time and the number of modifies entries and got better results

Data sanitization in association rule mining based on impact factor

Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved against association rule mining method. This process strongly rely on the minimizing the impact of data sanitization on the data utility by minimizing the number of lost patterns in the form of non-sensitive patterns which are not mined from sanitized database. This study proposes a data sanitization algorithm to hide sensitive patterns in the form of frequent itemsets from the database while controls the impact of sanitization on the data utility using estimation of impact factor of each modification on non-sensitive itemsets. The proposed algorithm has been compared with Sliding Window size Algorithm (SWA) and Max-Min1 in term of execution time, data utility and data accuracy. The data accuracy is defined as the ratio of deleted items to the total support values of sensitive itemsets in the source dataset. Experimental results demonstrate that proposed algorithm outperforms SWA and Max-Min1 in terms of maximizing the data utility and data accuracy and it provides better execution time over SWA and Max-Min1 in high scalability for sensitive itemsets and transactions

Introducing an algorithm for use to hide sensitive association rules through perturb technique

Due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. Association Rules Mining is one of the data mining techniques to extract useful patterns in the form of association rules. One of the main problems in applying this technique on databases is the disclosure of sensitive data by endangering security and privacy. Hiding the association rules is one of the methods to preserve privacy and it is a main subject in the field of data mining and database security, for which several algorithms with different approaches are presented so far. An algorithm to hide sensitive association rules with a heuristic approach is presented in this article, where the Perturb technique based on reducing confidence or support rules is applied with the attempt to remove the considered item from a transaction with the highest weight by allocating weight to the items and transactions. Efficiency is measured by the failure criteria of hiding, number of lost rules and ghost rules, and execution time. The obtained results of this study are assessed and compared with two known FHSAR and RRLR algorithms, based on two real databases (dense and sparse). The results indicate that the number of lost rules in all experiments are reduced by 47% in comparison with RRLR and reduced by 23% in comparison with FHSAR. Moreover, the other undesirable side effects, in this proposed algorithm in the worst case are equal to that of the base algorithms

Beyond subjective and objective in statistics

We argue that the words "objectivity" and "subjectivity" in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality, and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence. The advantage of these reformulations is that the replacement terms do not oppose each other. Instead of debating over whether a given statistical method is subjective or objective (or normatively debating the relative merits of subjectivity and objectivity in statistical practice), we can recognize desirable attributes such as transparency and acknowledgment of multiple perspectives as complementary goals. We demonstrate the implications of our proposal with recent applied examples from pharmacology, election polling, and socioeconomic stratification.Comment: 35 page

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy

Constellation Program Lessons Learned in the Quantification and Use of Aerodynamic Uncertainty

The NASA Constellation Program has worked for the past five years to develop a re- placement for the current Space Transportation System. Of the elements that form the Constellation Program, only two require databases that define aerodynamic environments and their respective uncertainty: the Ares launch vehicles and the Orion crew and launch abort vehicles. Teams were established within the Ares and Orion projects to provide repre- sentative aerodynamic models including both baseline values and quantified uncertainties. A technical team was also formed within the Constellation Program to facilitate integra- tion among the project elements. This paper is a summary of the collective experience of the three teams working with the quantification and use of uncertainty in aerodynamic environments: the Ares and Orion project teams as well as the Constellation integration team. Not all of the lessons learned discussed in this paper could be applied during the course of the program, but they are included in the hope of benefiting future projects

The Scored Society: Due Process for Automated Predictions

Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers—or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail—credit—the law focuses on credit history, not the derivation of scores from data. Procedural regularity is essential for those stigmatized by “artificially intelligent” scoring systems. The American due process tradition should inform basic safeguards. Regulators should be able to test scoring systems to ensure their fairness and accuracy. Individuals should be granted meaningful opportunities to challenge adverse decisions based on scores miscategorizing them. Without such protections in place, systems could launder biased and arbitrary data into powerfully stigmatizing scores

Differential evolution technique on weighted voting stacking ensemble method for credit card fraud detection

Differential Evolution is an optimization technique of stochastic search for a population-based vector, which is powerful and efficient over a continuous space for solving differentiable and non-linear optimization problems. Weighted voting stacking ensemble method is an important technique that combines various classifier models. However, selecting the appropriate weights of classifier models for the correct classification of transactions is a problem. This research study is therefore aimed at exploring whether the Differential Evolution optimization method is a good approach for defining the weighting function. Manual and random selection of weights for voting credit card transactions has previously been carried out. However, a large number of fraudulent transactions were not detected by the classifier models. Which means that a technique to overcome the weaknesses of the classifier models is required. Thus, the problem of selecting the appropriate weights was viewed as the problem of weights optimization in this study. The dataset was downloaded from the Kaggle competition data repository. Various machine learning algorithms were used to weight vote a class of transaction. The differential evolution optimization techniques was used as a weighting function. In addition, the Synthetic Minority Oversampling Technique (SMOTE) and Safe Level Synthetic Minority Oversampling Technique (SL-SMOTE) oversampling algorithms were modified to preserve the definition of SMOTE while improving the performance. Result generated from this research study showed that the Differential Evolution Optimization method is a good weighting function, which can be adopted as a systematic weight function for weight voting stacking ensemble method of various classification methods.School of ComputingM. Sc. (Computing