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

Analisis dan Implementasi Association Rule Mining pada Collaboration Recommender System Menggunakan Algoritma Association Rules for Recommender System (AR-CRS)

ABSTRAKSI: Ada banyak informasi yang bisa diolah dalam satu kumpulan data. Association rule mining merupakan salah satu cara untuk mencari informasi dari kumpulan data tersebut. Association rule mining yang umumnya digunakan dalam analisis keranjang belanja ternyata bisa diaplikasikan dalam area recommender system. Recommender system merupakan sebuah aplikasi yang merekomendasikan beberapa item yang mungkin sesuai dengan karakteristik pengguna. Sistem ini telah dipakai luas dalam bidang komersil saat ini. Tugas akhir ini mencoba melakukan analisis terhadap implementasi association rule mining pada recommender system dengan mengambil data EachMovie sebagai data uji. Hasil pengujian menunjukkan performansi parameter precision yang cukup baik jika rule yang digunakan adalah rule dengan 2-antecedent, sedangkan parameter performansi recall sangat baik jika rekomendasi menggunakan 1-antecedent.Kata Kunci : association rule mining, recommender system, Recall, Precision, FMeasureABSTRACT: There are many information that can be processed in a data collection. Association rule mining is one of the method used for discovering hiding information of data. Association rule mining which commonly used in market basket analysis actually can be implemented in recommender system area. Recommender system is an engine which recommends items that users may like and match to their profile. This application has been widely used in commercial site nowadays. This project try to analyse implementation of association rule mining in recommender system and using EachMovie dataset as a data testing. Testing result shows good precision when recommendation process use 2-antecedent rules, but recall will have good results when using 1-antecedent rules.Keyword: association rule mining, recommender system, Recall, Precision

DISTORTION-BASED HEURISTIC METHOD FOR SENSITIVE ASSOCIATION RULE HIDING

In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that  not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss

Investigations in Privacy Preserving Data Mining

Data Mining, Data Sharing and Privacy-Preserving are fast emerging as a field of the high level of the research study. A close review of the research based on Privacy Preserving Data Mining revealed the twin fold problems, first is the protection of private data (Data Hiding in Database) and second is the protection of sensitive rules (Knowledge) ingrained in data (Knowledge Hiding in the database). The first problem has its impetus on how to obtain accurate results even when private data is concealed. The second issue focuses on how to protect sensitive association rule contained in the database from being discovered, while non-sensitive association rules can still be mined with traditional data mining projects. Undoubtedly, performance is a major concern with knowledge hiding techniques. This paper focuses on the description of approaches for Knowledge Hiding in the database as well as discuss issues and challenges about the development of an integrated solution for Data Hiding in Database and Knowledge Hiding in Database. This study also highlights directions for the future studies so that suggestive pragmatic measures can be incorporated in ongoing research process on hiding sensitive association rules

Association rule hiding using integer linear programming

Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database

Protecting big data mining association rules using fuzzy system

Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process

Study of Association Rule Mining and Different Hiding Techniques

Data mining is the process of extracting hidden patterns from data. As more data is gathered,with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform this data into information. In this paper, we first focused on APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. We examined the data structure, implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. This algorithm has given us new capabilities to identify associations in large data sets. But a key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public, since among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. So, next we worked with some association rule hiding algorithms and examined their performances in order to analyze their time complexity and the impact that they have in the original database. We worked on two different side effects – one was the number of new rules generated during the hiding process and the other one was the number of non-sensitive rules lost during the process

Application of Text Message Held in Image Using Combination of Least Significant Bit Method and One Time Pad

Stenography and security are one of the techniques to develop art in securing data. Stenography has the most important aspect is the level of security in data hiding, which makes the third party unable to detect some information that has been secured. Usually used to hide textinformationThe (LSB) algorithm is one of the basic algorithms proposed by Arawak and Giant in 1994 to determine the frequent item set for Boolean association rules. A priory algorithm includes the type of association rules in data mining. The rule that states associations between attributes are often called affinity analysis or market basket analysis. OTP can be widely used in business. With the knowledge of text message, concealment techniques will make it easier for companies to know the number of frequencies of sales data, making it easier for companies to take an appropriate transaction action. The results of this study, hide the text message on the image (image) by using a combination of LSB and Otp methods

Preservation of confidential information privacy and association rule hiding for data mining: a bibliometric review

In this era of technology, data of business organizations are growing with acceleration. Mining hidden patterns from this huge database would benefit many industries improving their decision-making processes. Along with the non-sensitive information, these databases also contain some sensitive information about customers. During the mining process, sensitive information about a person can get leaked, resulting in a misuse of the data and causing loss to an individual. The privacy preserving data mining can bring a solution to this problem, helping provide the benefits of mined data along with maintaining the privacy of the sensitive information. Hence, there is a growing interest in the scientific community for developing new approaches to hide the mined sensitive information. In this research, a bibliometric review is carried out during the period 2010 to 2018 to analyze the growth of studies regarding the confidential information privacy preservation through approaches addressed to the hiding of association rules of data