A Study Survey of Privacy Preserving Data Mining

The main aim of Data mining techniques are to try to find out helpful patterns from the data that is big in quantity. These ideas or patterns are useful to find out some useful information. The abilities learned through fully knowledge mining approaches may contain confidential information about persons or trade. Upkeep of secrecy is a gigantic aspect of information mining also as a result be taught of attaining some information mining ambitions without dropping the secrecy of the individuals .The assessment of privacy preserving data mining (PPDM) algorithms must don't forget the penalties of those algorithms in mining the outcome along with retaining privacy. Inside the constraints of privateness, a couple of ways have been introduced however nonetheless this branch of exploration is in its early life .The success of privateness preserving data mining procedures is measured in phrases of its efficiency, data utility, degree of uncertainty or resistance to data mining procedures and so on. Nevertheless no privateness maintaining algorithm exists that outperforms all others on all feasible standards. Rather, an algorithm could participate in better than one other on one exact criterion. So, the aim of this paper is to show the current situation of privacy preserving knowledge mining framework and tactics

In organization large amount of data are collected daily and these data are used by the organization for data mining tasks. These data collected may contain sensitive attribute which not disclosed by un-trusted user. Privacy is very important when release the data for sharing purpose or mining. Privacy preserving data mining allow publishing data while same time it protect the sensitive or private data. For privacy preserving there are many technique like k-anonymity, cryptography, blocking based, data Perturbation etc. In this paper, various privacy preserving approaches in data sharing and their merits and demerits are analyzed.

International Journal of Engineering Research and

Enormous amount of detailed private data is recurrently collected and analysed by applications using data mining, sharing of these data is useful to the application users. While sharing the private data, privacy preserving is becoming an increasingly significant issue. Sequential pattern mining is the process of finding relevant pattern in the data set. Sequential pattern helps in envisaging the next event. Predicting the sequence datasets leads to violate the privacy and disclose sensitive patterns related to medical records, business secrets etc. This paper explores about different various techniques for privacy preserving data mining such as anonymity, randomization, secure multiparty computation, sequential pattern hiding.

2015

Privacy preserving Data mining is an emerging technology which performs data mining operations in centralized or distributed data in a secured manner to preserve sensitive data. A number of techniques such as randomization, secured sum algorithm and k-anonymity have been suggested in order to perform privacy-preserving data mining. In this paper, a survey on recent researches made on Privacy preserving data mining techniques with Fuzzy logic, neural network learning, secured sum and encryption algorithms is presented. This will enable to understand the challenges faced in Privacy preserving data mining and also helps to identify best techniques suitable for various data environment. Keywords— Multi level Trust Privacy Preserving Data mining (MLT-PPDM), Neural Network Learning (NNL), Non negative Matrix Factorization (NMF), Probabilistic Neural Network (PNN) Privacy Preserving Data Mining (PPDM), Privacy Preserving Data Publishing (PPDP), Secure Multiparty Computation (SMC)

In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.

IJEMR, 2017

Now a day’s information is played major role in decision making in an organization. We are in the world of information processing society. Data is the major valuable resource of any business or organization. There is a huge amount of sensitive data produced by various business operational applications. Sharing information between various sources through authorized channel is an important task. Data Mining is kind knowledge discovery system, through this data can extract from different sources. While sharing information through different channels or extracting information from different external sources, the key factor is protecting data from unauthorized accesses. This paper presents a brief idea about protecting extracted data of data mining system without loss of processing data using Privacy Preserving techniques and its comparison.

ACM Sigmod …, 2004

We provide here an overview of the new and rapidly emerging research area of privacy preserving data mining. We also propose a classification hierarchy that sets the basis for analyzing the work which has been performed in this context. A detailed review of the work accomplished in this area is also given, along with the coordinates of each work to the classification hierarchy. A brief evaluation is performed, and some initial conclusions are made.

ijettjournal.org

Recent interest in the collection and monitoring of data using data mining technology for the purpose of security and business-related applications has raised serious concerns about privacy issues. For example, mining health care data for the detection of disease outbreaks may require analyzing clinical records and pharmacy transaction data of many individuals over a certain area. However, releasing and gathering such diverse information belonging to different parties may violate privacy laws and eventually be a threat to civil liberties. Privacy preserving data mining strives to provide a solution to this dilemma. It aims to allow useful data patterns to be discovered without compromising privacy. This paper presents an brief overview on preserving data mining methodologies.

International Journal of Computer Applications, 2016

Privacy has become crucial in knowledge based applications. Proper integration of individual privacy is essential for data mining operations. This privacy based data mining is important for sectors like Healthcare, Pharmaceuticals, Research, and Security Service Providers, to name a few. The main categorization of Privacy Preserving Data Mining (PPDM) techniques falls into Perturbation, Secure Sum Computations and Cryptographic based techniques. There exist tradeoffs between privacy preservation and information loss for generalized solutions. The authors of the paper present an extensive survey of PPDM techniques, their classification and give a preliminary implication of technique to be used under specific scenarios.

International Journal of Recent Trends in Engineering and Research, 2018

With the development of network, data collection and storage technology, the use and sharing of large amounts of data has become possible. Once the data and information accumulated, it will become the wealth of information. However, traditional data mining techniques and algorithms directly operated on the original data set, which will cause the leakage of privacy data. At the same time, large amounts of data implicate the sensitive knowledge that their disclosure cannot be ignored to the competitiveness of enterprise. In order to overcome these problems, Privacy Preserving Data Mining (PPDM) techniques are developed. Traditional PPDM techniques suffer from different types of attacks and loss of information. In this paper an alternative method was proposed which provides less information loss and more privacy.