Sensitive Frequent Itemsets Research Articles

Hiding sensitive frequent itemsets by item removal via two-level multi-objective optimization

Hiding sensitive frequent itemsets by item removal via two-level multi-objective optimization

MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data

This paper presents a novel ‘MapReduce Based Optimum Victim Item and Transaction Selection Approach (MR-OVnTSA)’ that provides a feasible and intelligent solution for protecting sensitive frequent itemsets present in big data. The approach advocates to resolve the captious challenges, existing knowledge hiding algorithms are encountering. The proposed solution optimally minimizes the side effect of hiding process on non-sensitive information, and maintains a balance between knowledge and privacy as well as handles the exponential growth in data volume efficiently. The algorithm plugs the most optimum item and transaction as victim, by intelligently analyzing their coverage value i.e. it chooses one with maximal impact on sensitive knowledge but minimal on non-sensitive information. Further, the MapReduce version of the proposed scheme resolves the issue of non-feasibility by processing large-scale data (big data) in a parallel fashion. Experiments have been demonstrated over real and synthetically generated large-scale datasets. Results evince that the proffered scheme is much more efficient and maintains the balance between the privacy preservation, data quality maintenance, and CPU time, when dealing with large voluminous big datasets compared to existing knowledge hiding techniques.

Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction

The process of frequent itemset mining (FIM) within large-scale databases plays a significant part in many knowledge discovery tasks, where, however, potential privacy breaches are possible. Privacy preserving frequent itemset mining (PPFIM) has thus drawn increasing attention recently, where the ultimate goal is to hide sensitive frequent itemsets (SFIs) so as to leave no confidential knowledge uncovered in the resulting database. Nevertheless, the vast majority of the proposed methods for PPFIM were merely based on database perturbation, which may result in a significant loss of data utility in order to conceal all SFIs. To alleviate this issue, this paper proposes a database reconstruction-based algorithm for PPFIM (DR-PPFIM) that can not only achieve a high degree of privacy but also afford a reasonable data utility. In DR-PPFIM, all SFIs with related frequent itemsets are first identified for removing in the pre-sanitize process by implementing a devised sanitize method. With the remained frequent itemsets, a novel database reconstruction scheme is proposed to reconstruct an appropriate database, where the concepts of inverse frequent itemset mining (IFIM) and database extension are efficiently integrated. In this way, all SFIs are able to be hidden under the same mining threshold while maximizing the data utility of the synthetic database as much as possible. Moreover, we also develop a further hiding strategy in DR-PPFIM to further decrease the significance of SFIs with the purpose of reducing the risk of disclosing confidential knowledge. Extensive comparative experiments are conducted on real databases to demonstrate the superiority of DR-PPFIM in terms of maximizing the utility of data and resisting potential threats.

Efficient hiding of confidential high-utility itemsets with minimal side effects

Privacy preserving data mining (PPDM) is an emerging research problem that has become critical in the last decades. PPDM consists of hiding sensitive information to ensure that it cannot be discovered by data mining algorithms. Several PPDM algorithms have been developed. Most of them are designed for hiding sensitive frequent itemsets or association rules. Hiding sensitive information in a database can have several side effects such as hiding other non-sensitive information and introducing redundant information. Finding the set of itemsets or transactions to be sanitised that minimises side effects is an NP-hard problem. In this paper, a genetic algorithm (GA) using transaction deletion is designed to hide sensitive high-utility itemsets for PPUM. A flexible fitness function with three adjustable weights is used to evaluate the goodness of each chromosome for hiding sensitive high-utility itemsets. To speed up the evolution process, the pre-large concept is adopted in the designed algorithm. It reduces the number of database scans required for verifying the goodness of an evaluated chromosome. Substantial experiments are conducted to compare the performance of the designed GA approach (with/without the pre-large concept), with a GA-based approach relying on transaction insertion and a non-evolutionary algorithm, in terms of execution time, side effects, database integrity and utility integrity. Results demonstrate that the proposed algorithm hides sensitive high-utility itemsets with fewer side effects than previous studies, while preserving high database and utility integrity.

Femtocell Interference and Probability of Connection in different Areas and Various SINR Threshold Values at Downlink

Many privacy-preserving data mining algorithms attempt to hide what database owners consider as sensitive. Specifically, in the association rules domain, many of these algorithms are based on item restriction methods; that is, removing items from some transactions in order to hide sensitive frequent itemsets. There are two known algorithms for that purpose, ISL (Increase Support of Left) and DSR (Decrease Support of Right). Both of them make use of user-specified values for minimum support threshold and minimum confidence threshold as input. Since ISL and DSR techniques aim at hiding all sensitive rules, they cannot avoid the undesired side effects. In this paper, a new algorithm for hiding sensitive rules is proposed based on ISL and DSR. It depends on decreasing the confidence of the sensitive rule by dealing with both Left Hand Side (LHS) and Right Hand Side (RHS) according to the ratio between them. This technique introduces introduce a reasonable side effect (lost and new rules) as compared with ISL and DSR algorithms which introduces a high number of new rules and/or lost rules. Experimental results have shown that the proposed algorithm effectively reduces the side effects which occur due to the hide operation when compared with the ISL and DSR algorithms and that it gives a good hiding ratio for the sensitive rules.

A transversal hypergraph approach for the frequent itemset hiding problem

We propose a methodology for hiding all sensitive frequent itemsets in a transaction database. Our methodology relies on a novel technique that enumerates the minimal transversals of a hypergraph in order to induce the ideal border between frequent and sensitive itemsets. The ideal border is then utilized to formulate an integer linear program (ILP) that answers whether a feasible sanitized database that attains the ideal border, exists. The solution of the program identifies the set of transactions that need to be modified (sanitized) so that the hiding can be achieved with the maximum accuracy. If no solution exists, we modify the ILP by relaxing the constraints needed to be satisfied so that the sanitized database preserves the privacy with guarantee but with minimum effect in data quality. Experimental evaluation of the proposed approach on a number of real datasets has shown that the produced sanitized databases exhibit higher accuracy when compared with the solutions of other well-known approaches.

Hiding Sensitive Frequent Itemsets over Privacy Preserving Distributed Data

Data mining is the process of extracting hidden patterns from data. One of the most important activities in data mining is the association rule mining and the new head for data mining research area is privacy of mining. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Data mining can be applied on centered or distributed databases. Most efficient approaches for mining distributed databases suppose that all of the data at each site can be shared. Privacy concerns may prevent the sites from directly sharing the data, and some types of information about the data. Privacy Preserving Data Mining (PPDM) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. In this paper, the problem of privacy preserving association rule mining in horizontally distributed database is addressed by proposing a system to compute a global frequent itemsets or association rules from different sites without disclosing individual transactions. Indeed, a new algorithm is proposed to hide sensitive frequent itemsets or sensitive association rules from the global frequent itemsets by hiding them from each site individually. This can be done by modifying the original database for each site in order to decrease the support for each sensitive itemset or association rule. Experimental results show that the proposed algorithm hides rules in a distributed system with the good execution time, and with limited side effects. Also, the proposed system has the capability to calculate the global frequent itemsets from different sites and preserves the privacy for each site.

민감한 빈발항목집합을 숨기기 위한 경계기반 HSFI 알고리즘

민감한 정보 숨기기 알고리즘은 민감한 정보를 보호하기 위하여 트랜잭션 데이터베이스를 삭제한다. 데이터 변경은 삭제 접근 방법들 중 하나이다. 민감한 정보를 숨기는 이전 연구들은 결과 데이터베이스의 품질을 유지하기 위해 서로 다른 휴리스틱 알고리즘을 적용했다. 그러나 민감한 정보를 숨기는 과정에서 변경되는 항목집합에 대한 영향을 평가하거나 숨겨지는 항목을 감소시키는 연구들은 미흡하였다. 본 논문에서는 민감한 빈발 항목집합을 숨기기 위하여 경계기반의 HSFI(Hiding Sensitive Frequent Itemsets) 알고리즘을 제안한다. 본 알고리즘에서 FP-Tree의 노드 정보는 기존과는 다르게 빈발 항목집합 생성단계에서 트랜잭션 정보와 민감 정보, 경계 정보를 모두 구성하며, 숨기는 과정에서 비민감한 빈발 항목집합의 영향을 줄이기 위하여 경계를 사용하였다. 본 논문의 예시 트랜잭션 데이터베이스에 HSFI를 적용한 결과, 손실 항목을 크게 감소시킴으로써 기존 방법들에 비해 효과적임을 증명하였고, 보다 개선된 데이터베이스의 품질을 유지할 수가 있었다. This paper suggests the border based HSFI algorithm to hide sensitive frequent itemsets. Node formation of FP-Tree which is different from the previous one uses the border to minimize the impacts of nonsensitive frequent itemsets in hiding process, including the organization of sensitive and border information, and all transaction as well. As a result of applying HSFI algorithms, it is possible to be the example transaction database, by significantly reducing the lost items, it turns out that HSFI algorithm is more effective than the existing algorithm for maintaining the quality of more improved database.

민감한 빈발 항목집합 숨기기 위한 확장 빈발 패턴 트리

최근 기업 간 또는 기관 사이의 데이터 공유는 업무 협력을 위해서 필요한 사안이 되고 있다. 이 과정에서 기업이 데이터베이스를 계열회사에 공개했을 때 민감한 정보가 유출되는 문제점이 발행할 수도 있다. 이런 문제를 해결하기 위해서 민감한 정보를 데이터베이스로부터 숨기는 일이 필요하게 되었다. 민감한 정보를 숨기는 이전 연구들은 결과 데이터베이스의 품질을 유지하기 위해 다른 휴리스틱 알고리즘을 적용했다. 그러나 민감한 정보를 숨기는 과정에서 변경되는 항목집합에 대한 영향을 평가하거나 숨겨지는 항목을 최소화하는 연구들은 미흡하였다. 본 논문에서는 민감한 빈발 항목집합을 숨기기 위하여 FP-Tree(Frequent Pattern Tree)기반의 확장 빈발 패턴트리(Extended Frequent Pattern Tree, eFP-Tree)를 제안한다. eFP-Tree의 노드 구성은 기존과는 다르게 빈발 항목집합 생성단계에서 트랜잭션 정보와 민감 정보, 경계 정보를 모두 구성하며, 숨기는 과정에서 비민감한 빈발 항목집합의 영향을 최소화하기 위하여 경계를 사용하였다. 본 논문의 예시 트랜잭션 데이터베이스에 eFP-Tree를 적용한 결과, 손실 항목을 평균 10%이하로 최소화하여 기존 방법들에 비해 효과적임을 증명하였고, 데이터베이스의 품질을 최적으로 유지할 수가 있었다. Recently, data sharing between enterprises or organizations is required matter for task cooperation. In this process, when the enterprise opens its database to the affiliates, it can be occurred to problem leaked sensitive information. To resolve this problem it is needed to hide sensitive information from the database. Previous research hiding sensitive information applied different heuristic algorithms to maintain quality of the database. But there have been few studies analyzing the effects on the items modified during the hiding process and trying to minimize the hided items. This paper suggests eFP-Tree(Extended Frequent Pattern Tree) based FP-Tree(Frequent Pattern Tree) to hide sensitive frequent itemsets. Node formation of eFP-Tree uses border to minimize impacts of non sensitive frequent itemsets in hiding process, by organizing all transaction, sensitive and border information differently to before. As a result to apply eFP-Tree to the example transaction database, the lost items were less than 10%, proving it is more effective than the existing algorithm and maintain the quality of database to the optimal.

Hiding Sensitive Frequent Itemsets by a Border-Based Approach

Nowadays, sharing data among organizations is often required during the business collaboration. Data mining technology has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that non-sensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. Unlike previous work, we consider the quality of the sanitized database especially on preserving the non-sensitive frequent itemsets. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach.