International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02| Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Privacy Preserving Data Mining Using Inverse Frequent ItemSet Mining Approach Ms. Ashwini S. Chavan1, Prof. Rahul P. Mirajkar2 1Student,
Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, Maharashtra, India 2Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The paper presents architecture for protection
Key words: Data mining, sensitive information, privacy preserving data mining (PPDM), inverse frequent itemset mining, protection.
collector gets data from data providers so as to support the subsequent data mining operations. In order to invent useful knowledge which is expected by the decision maker, the data miner applies data mining algorithms to data obtained from data collector. A decision maker can get the data mining results directly from the data miner, or from some Information Transmitter. The focus of this topic is to achieve privacy at data miner level. Data miner will get data to mine from data collector to be able to in not unique layout and by means of applying one-of-a-kind data mining strategies; data miner can discover sensitive information. So venture of data miner is to hold the privacy of received result and pass the consequences to decision maker that doesn't bring about any security breach. Several studies on PPDM have been conducted [5] [6]. But none of the modern-day proposals provide privacy to unwanted disclosure of sensitive information. The paper presents a system architecture that provides privacy by the use data mining algorithms without affecting the security of sensitive information contained in the data.
1. INTRODUCTION
2. LITERATURE SURVEY
Mining of data is the process of discovering interesting patterns and knowledge from large amounts of data [1]. The information collected by data mining can be very important to many applications, despite that there is another concern on focus of the privacy threats posed by data mining [2]. To address the privacy troubles in data mining, Privacy preserving data mining (PPDM) has received a top notch improvement in latest years[3][4]. The objective of PPDM is to safeguard sensitive information against the disclosure of data by maintaining its utility. The PPDM Consideration is two-fold. First, sensitive raw data e.g. individual’s ID card number, cell phone number should not be directly used for mining. Second, sensitive mining results whose disclosure will cause privacy violation should be excluded. In PPDM process four different types of users are involved namely data provider, data collector, and data miner and decision maker. Each one has their specific role in the process. A data provider owns a few data from which precious information can be extracted. Data
Several theoretical approaches for privacy preserving of data have been proposed in the literature.
of information against third party attack. Individual sensitive information is in danger with increasing technologies of data mining. A new research data mining topic, known as privacy-preserving data mining (PPDM), has been tremendously studied in recent years. Privacy preserving data mining (PPDM) aims to maintain privacy of individual data or sensitive information without sacrificing the utility of the data. Currently, privacy preserving data mining (PPDM) mainly consciousness on a way to reduce the privacy threat delivered by way of data mining operations, even as in truth, unwanted disclosure of sensitive information may manifest in the system of data collecting, data publishing, and information (i.e. The data mining effects) delivering. To this effect, paper proposes a technique called inverse frequent itemset mining approach that will help to protect sensitive information without loss of data.
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2.1 Related Research B. Fung, K. Wang, R. Chen, and P. S. Yu et al [6] introduced techniques to protect the data. Data in its original form have sensitive information about person, and publishing such data will violate individual privacy. Privacy-preserving data publishing (PPDP) explains methods and tools for publishing useful information while preserving data privacy. The author introduces different schemes to PPDP, study the challenges in practical data publishing, and clarify the differences and requirements that distinguish PPDP from other related problems. T. Mielikainen [7] introduces a well known technique called frequent set mining to describe binary data. However, it is an open problem how difficult it is to make opposite the frequent set mining. The author analyze the computational complexity of the problem of ISO 9001:2008 Certified Journal
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