Privacy Preserving Utility Mining Research Articles

Fast privacy-preserving utility mining algorithm based on utility-list dictionary

Fast privacy-preserving utility mining algorithm based on utility-list dictionary

Efficient privacy preserving algorithms for hiding sensitive high utility itemsets

Utility-driven mining is a powerful data mining technique that aims to extract significant and valuable knowledge from different kinds of datasets. Nevertheless, analyzing datasets with sensitive or private information may raise security and privacy concerns. To balance utility maximization and privacy preservation, Privacy Preserving Utility Mining (PPUM) has been presented. The primary objective of PPUM algorithms is to conceal the sensitive knowledge that can be found via the application of utility mining algorithms to sensitive data. However, the current PPUM literature shows a paucity of privacy preserving algorithms scalable and efficient enough to handle large and dense datasets. Based on this perspective, this paper proposes three heuristic-based algorithms, namely Selecting the Most Real item sensitive utility First (SMRF), Selecting the Least Real item sensitive utility First (SLRF), and Selecting the most Desirable Item First (SDIF), to efficiently conceal all Sensitive High utility Itemsets (SHIs) while mitigating the projected detrimental impact on the non-sensitive information. The proposed algorithms rely on a novel concept, called Real Item Sensitive Utility (RISU), to effectively select a definite victim item for each SHI during the whole sanitization process. Furthermore, a new sensitive dataset sorting technique is proposed to reduce the time needed to find suitable transactions for sanitization. Through comprehensive experimental evaluations with state-of-the-arts, the viability of the proposed three algorithms was testified. The acquired findings clearly demonstrate the efficacy of the proposed algorithms in terms of reducing the sanitization time and side effects.

A Fast Algorithm for Privacy-Preserving Utility Mining

Utility mining (UM) is an efficient technique for data mining which aim to discover critical patternsfrom various types of database. However, mining data can reveal sensitive information of individuals. Privacy preserving utility mining (PPUM) emerges as an important research topic in recent years. In the past, integer programming approach was developed to hide sensitive knowledge in a database. This approach required a significant amount of time for preprocessing and formulating a constraint satisfaction problem (CSP). To address this problem, we proposed a new algorithm based on a hash data structure which performs more quickly in itemsets filtering and problem modeling. Experiment evaluations are conducted on real world and synthetic datasets.

Efficient algorithms for victim item selection in privacy-preserving utility mining

High-utility itemset mining has evolved as an essential and captivating research topic. It aims to extract the patterns/itemsets having high utility value; hence, they are called high utility itemsets (HUIs). From a business perspective, a utility can be the benefit associated with the sale of a particular item or the usefulness or satisfaction that a customer experiences from a product. The economic utilities are helpful to evaluate the drivers behind a customer’s purchase decision. The advances in information technology have enabled us to access the datasets related to various domains like health care, stock market, market-basket, education and bioinformatics. Companies strive to increase the utility value of their products and share their customer’s transactions data to extract high utility patterns to achieve global customer insights. However, this can lead to massive security and privacy risk if their competitors misuse the patterns that can disclose their confidential information. Privacy-preserving utility mining (PPUM) is a branch of privacy-preserving data mining (PPDM) that presents various algorithms which intend to hide sensitive high utility itemsets (SHUIs) and maintain a balance between utility-maximizing and privacy-preserving. In this paper, two SHUIs hiding algorithms, MinMax and Weighted, are proposed with three variants of each algorithm. Experiments on various datasets show that proposed algorithms perform better than the existing SHUIs hiding algorithms as fewer distortions of non-sensitive knowledge occur. This study uses six performance evaluating metrics to assess the proposed algorithms against compared algorithms.

WITHDRAWN: Security protocol of sensitive high utility itemset hiding in shared IoT environments

Abstract As Internet Technology (IT) rapidly grows, it is necessary to protect private and confidential information of collected data from both smart and Internet of Things (IoT) devices. It becomes a necessity to protect and hide confidential or private information for data sharing or publishing between collaborators and partners. The concept of Privacy-Preserving Data Mining, known as PPDM, is created to clean the database for hiding sensitive information. However, it applies only to binary databases. To handle Privacy-Preserving Utility Mining, known as PPUM, we present a Maximum Sensitive Utility-Maximum Sensitive conflIct algorithm, known as MSU-MSI, to find conflicting items within sensitive high-utility itemsets for sanitization of data. Transactions with sensitive high-utility itemsets are first projected for the sanitization process. Next, the number of conflicting items within the itemsets is calculated, and the designed progress is used to reduce the quantity value of processed items for sanitization. In our experimental results, the designed algorithm is compared with the standard algorithms like HHUIF, MSICF, and the state-of-the-art MSU-MAX and MSU-MIN approaches. Finally, the proposed MSU-MSI is shown to achieve better performance in terms of missing cost, especially in extreme datasets (very dense or very sparse), a true testament to the process. Furthermore, the designed MSU-MSI achieved an excellent performance in terms of database structure similarity and database utility similarity with a very slight difference compared to the previously achieved top results.

An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

High-utility pattern mining is an effective technique that extracts significant information from varied types of databases. However, the analysis of data with sensitive private information may cause privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, privacy-preserving utility mining (PPUM) has become an important research topic in recent years. The MSICF algorithm is a sanitization algorithm for PPUM. It selects the item based on the conflict count and identifies the victim transaction based on the concept of utility. Although MSICF is effective, the heuristic selection strategy can be improved to obtain a lower ratio of side effects. In our paper, we propose an improved sanitization approach named the Improved Maximum Sensitive Itemsets Conflict First Algorithm (IMSICF) to address this issue. It dynamically calculates conflict counts of sensitive items in the sanitization process. In addition, IMSICF chooses the transaction with the minimum number of nonsensitive itemsets and the maximum utility in a sensitive itemset for modification. Extensive experiments have been conducted on various datasets to evaluate the effectiveness of our proposed algorithm. The results show that IMSICF outperforms other state-of-the-art algorithms in terms of minimizing side effects on nonsensitive information. Moreover, the influence of correlation among itemsets on various sanitization algorithms’ performance is observed.

Effective sanitization approaches to protect sensitive knowledge in high-utility itemset mining

For mutual benefit, data is shared among business organizations. However, this may result in privacy and security threats. To address this issue, privacy-preserving data mining is presented to sanitize the original database to hide all sensitive knowledge. Privacy-preserving utility mining is an extension of privacy-preserving data mining, the objective of which is to hide all sensitive high-utility itemsets and minimize the side effects on non-sensitive knowledge caused by the sanitization process. In this paper, three heuristic algorithms for privacy-preserving utility mining are proposed, namely, Selecting Maximum Utility item first (SMAU), Selecting Minimum Utility item first (SMIU) and Selecting Minimum Side Effects item first (SMSE). The quality of the database is well maintained because all of the proposed algorithms consider the side effects on the non-sensitive itemsets. Furthermore, to avoid performing multiple database scans, two table structures, T-table and HUI-table, are adopted to accelerate the hiding process by only scanning the database twice. The experimental results show that the proposed approaches successfully conceal all sensitive itemsets with fewer distortions of non-sensitive knowledge. Moreover, the influence of the database density on the proposed approaches is observed.

A novel algorithm for privacy preserving utility mining based on integer linear programming

As an important research topic, high-utility itemset mining (HUIM) has, of late years, attracted increasing attention, where both the significance and quantity factors of items are taken into account to mine high-utility itemsets (HUIs). Privacy breaches have always been a major issue existing in the field of data mining, which usually inevitably arise, especially when private data collections are publicly published or shared by organizations. To tackle this problem, plentiful methodologies regarding privacy-preserving data mining (PPDM) have been proposed. Due to the high practicality of HUIM, in recent years, privacy-preserving utility mining (PPUM) has become a popular research orientation in PPDM. The main goal of PPUM is to hide sensitive HUIs (SHUIs) so as to leave no confidential information uncovered in the resulting sanitized database. However, all the previously proposed approaches have suffered from the defect of introducing numerous side effects by performing database perturbation. To alleviate this issue, in this paper, a novel algorithm based on integer linear programming (ILP) is proposed to obtain a lower ratio of side effects produced in the hiding process while does not reveal any sensitive information in the sanitized database. We formulate the hiding process as a constraint satisfaction problem (CSP), which pursuing the protection of SHUIs as well as the minimization of side effects. A solution to the hiding problem is expected to be obtained by exploiting ILP technique to solve the mapped problem, which properly indicates the processing manner of perturbation operation. In addition, a relaxation procedure is also adopted in the designed algorithm to provide an approximate solution of the CSP when the optimal one does not exist. Extensive experimental evaluations between our proposed method and other state-of-the-art algorithms are conducted on several real-world datasets. The comparative results demonstrate the superiorities of the proposed algorithm with respect to running time and the ability to minimize side effects.

Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining

High-Utility Itemset Mining (HUIM) is an extension of frequent itemset mining, which discovers itemsets yielding a high profit in transaction databases (HUIs). In recent years, a major issue that has arisen is that data publicly published or shared by organizations may lead to privacy threats since sensitive or confidential information may be uncovered by data mining techniques. To address this issue, techniques for privacy-preserving data mining (PPDM) have been proposed. Recently, privacy-preserving utility mining (PPUM) has become an important topic in PPDM. PPUM is the process of hiding sensitive HUIs (SHUIs) appearing in a database, such that the resulting sanitized database will not reveal these itemsets. In the past, the HHUIF and MSICF algorithms were proposed to hide SHUIs, and are the state-of-the-art approaches for PPUM. In this paper, two novel algorithms, namely Maximum Sensitive Utility-MAximum item Utility (MSU-MAU) and Maximum Sensitive Utility-MInimum item Utility (MSU-MIU), are respectively proposed to minimize the side effects of the sanitization process for hiding SHUIs. The proposed algorithms are designed to efficiently delete SHUIs or decrease their utilities using the concepts of maximum and minimum utility. A projection mechanism is also adopted in the two designed algorithms to speed up the sanitization process. Besides, since the evaluation criteria proposed for PPDM are insufficient and inappropriate for evaluating the sanitization performed by PPUM algorithms, this paper introduces three similarity measures to respectively assess the database structure, database utility and item utility of a sanitized database. These criteria are proposed as a new evaluation standard for PPUM.

High utility-itemset mining and privacy-preserving utility mining

Summary In recent decades, high-utility itemset mining (HUIM) has emerging a critical research topic since the quantity and profit factors are both concerned to mine the high-utility itemsets (HUIs). Generally, data mining is commonly used to discover interesting and useful knowledge from massive data. It may, however, lead to privacy threats if private or secure information (e.g., HUIs) are published in the public place or misused. In this paper, we focus on the issues of HUIM and privacy-preserving utility mining (PPUM), and present two evolutionary algorithms to respectively mine HUIs and hide the sensitive high-utility itemsets in PPUM. Extensive experiments showed that the two proposed models for the applications of HUIM and PPUM can not only generate the high quality profitable itemsets according to the user-specified minimum utility threshold, but also enable the capability of privacy preserving for private or secure information (e.g., HUIs) in real-word applications.

A fast perturbation algorithm using tree structure for privacy preserving utility mining

As one of the important approaches in privacy preserving data mining, privacy preserving utility mining has been studied to find more meaningful results while database privacy is ensured and to improve algorithm efficiency by integrating fundamental utility pattern mining and privacy preserving data mining methods. However, its previous approaches require a significant amount of time to protect the privacy of data holders because they conduct database scanning operations excessively many times until all important information is hidden. Moreover, as the size of a given database becomes larger and a user-specified minimum utility threshold becomes lower, their performance degradation may be so uncontrollable that they cannot operate normally. To solve this problem, we propose a fast perturbation algorithm based on a tree structure which more quickly performs database perturbation processes for preventing sensitive information from being exposed. We also present extensive experimental results between our proposed method and state-of-the-art algorithms using both real and synthetic datasets. They show the proposed method has not only outstanding privacy preservation performance that is comparable to the previous ones but also 5–10 times faster runtime than that of the existing approaches on average. In addition, the proposed algorithm guarantees better scalability than that of the latest ones with respect to databases with the characteristics of gradually increasing attributes and transactions.

A GA-based approach to hide sensitive high utility itemsets.

A GA-based privacy preserving utility mining method is proposed to find appropriate transactions to be inserted into the database for hiding sensitive high utility itemsets. It maintains the low information loss while providing information to the data demanders and protects the high-risk information in the database. A flexible evaluation function with three factors is designed in the proposed approach to evaluate whether the processed transactions are required to be inserted. Three different weights are, respectively, assigned to the three factors according to users. Moreover, the downward closure property and the prelarge concept are adopted in the proposed approach to reduce the cost of rescanning database, thus speeding up the evaluation process of chromosomes.

An Optimization based Modified Maximum Sensitive Item-Sets Conflict First Algorithm (MSICF) for Hiding Sensitive Item-Sets

In privacy preserving data mining, utility mining plays an important role. In privacy preserving utility mining, some sensitive itemsets are hidden from the database according to certain privacy policies. Hiding sensitive itemsets from the adversaries is becoming an important issue nowadays. The existing paper utilized two algorithms; such as HHUIF and MSICF are conceal the sensitive itemsets, so that the adversaries cannot mine them from the modified database. But, the performance of this method lacks if the utility value of the items are same. To solve this problem, in this paper a Modified MSICF algorithm (MMSICF) is proposed. The proposed MMSICF algorithm is a modified version of existing MSICF algorithm. The MMSICF algorithm computes the sensitive itemsets by utilizing the user defined utility threshold value. The threshold value selection plays a major role in this paper and it is determined by the hybridization of Artificial Bee Colony (ABC) and Genetic Algorithm (GA). In order to hide the sensitive itemsets, the frequency value of the items is changed. The proposed MMSICF reduces the computation complexity as well as improves the hiding performance of the itemsets. The algorithm is implemented and the resultant itemsets are compared against the itemsets that are obtained from the conventional privacy preserving utility mining algorithms. General Terms Data Mining, Privacy

HHUIF and MSICF: Novel algorithms for privacy preserving utility mining

Privacy preserving data mining (PPDM) is a popular topic in the research community. How to strike a balance between privacy protection and knowledge discovery in the sharing process is an important issue. This study focuses on privacy preserving utility mining (PPUM) and presents two novel algorithms, HHUIF and MSICF, to achieve the goal of hiding sensitive itemsets so that the adversaries cannot mine them from the modified database. The work also minimizes the impact on the sanitized database of hiding sensitive itemsets. The experimental results show that HHUIF achieves lower miss costs than MSICF on two synthetic datasets. On the other hand, MSICF generally has a lower difference ratio than HHUIF between original and sanitized databases.