A Review on Privacy Preserving Data Mining (original) (raw)

Comprehensive Review On Privacy Preserving Data Mining Techniques And Methods

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.

An Efficient Approach of Privacy Preserving Data Mining

In many organizations large amount of data are collected. These data are sometimes used by the organizations for data mining tasks. However, the data collected may contain private or sensitive information which should be protected. Privacy protection is an important issue if we release data for the mining or sharing purpose. Privacy preserving data mining techniques allow publishing data for the mining purpose while at the same time preserve the private information of the individuals. Many techniques have been proposed for privacy preservation but they suffer from various types of attacks and information loss. In this paper we proposed an efficient approach for privacy preservation in data mining. Our technique protects the sensitive data with less information loss which increase data usability and also prevent the sensitive data for various types of attack. Data can also be reconstructed using our proposed technique.

A Review Study on the Privacy Preserving Data Mining Techniques and Approaches

International Journal of Computer Science and Telecommunications (IJCST) , 2013

With the extensive amount of data stored in databases and other repositories it is very important to develop a powerful and effective mean for analysis and interpretation of such data for extracting the interesting and useful knowledge that could help in decision making. Data mining is such a technique which extracts the useful information from the large repositories. Knowledge discovery in database (KDD) is another name of data mining. Privacy preserving data mining techniques are introduced with the aim of extract the relevant knowledge from the large amount of data while protecting the sensible information at the same time. In this paper we review on the various privacy preserving data mining techniques like data modification and secure multiparty computation based on the different aspects. We also analyze the comparative study of all techniques followed by the future research work.

Privacy Preservation of Data in Data mining using K-anonymity and Randomization Method

Increasing the business prospective the sharing of data is the most important. But when Sensitive data are share between two parties at that time the privacy of data is the major problem. In day to day life the Sharing, transferring, mining and publishing data are the major factor in privacy preservation. When sensitive data are share between two parties then the privacy of data is the major problem. The main aim of the privacy preservation is protecting the sensitive information in data while extracting knowledge from large amount of data. There are many techniques are use in privacy preservation like k-anonymity, l-diversity, t-closeness, blocking based method and cryptography techniques. Privacy preserving techniques available but still they have shortcomings. Like Anonymity technique gives privacy protection and usability of data but it suffers from homogeneity and background attack. Blocking method suffers from information loss and random perturbation technique does not provide usability of data. Cryptography technique gives privacy protection but does not provide usability of data and it requires more computational overhead. So in this work we use the k-anonymity method to prevent our data and we can get better accuracy as compare to previously used methods.

A Survey on Privacy Preserving Data Mining Techniques

Data mining is the extraction of the important patterns or information from large amount of data, which is used for decision making in future work. But the process of data collection and data dissemination may cause the serious damage for any organization, however may causes the privacy concerns. Sensitive or personal information of individuals, industries and organization must be kept private before it is shared or published. Thus privacy preserving data mining has become an important issue to efficiently hide sensitive information. Many numbers of methods and techniques have been proposed for privacy preserving data mining for hiding sensitive information. In this paper we provides our own overview which has taken from previous paper on privacy preserving data mining.

A Review Study On Privacy Preserving Data Mining Techniques And Approaches

Data is the central asset of today's dynamically operating organization and their business. This data is usually stored in database. A major consideration is applied on the security of that data from the unauthorized access and intruders. Data encryption is a strong option for security of data in database and especially in those organizations where security risks are high. But there is a potential disadvantage of performance degradation. When we apply encryption on database then we should compromise between the security and efficient query processing. The work of this paper tries to fill this gap. It allows the users to query over the encrypted column directly without decrypting all the records. It's improves the performance of the system. The proposed algorithm works well in the case of range and fuzzy match queries.

A survey on privacy preserving data mining

… International Workshop on Database …, 2009

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes .So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In recent years, privacy preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. We discuss method for randomization, kanonymization, and distributed privacy preserving data mining. Knowledge is supremacy and the more knowledgeable we are about information break-in, we are less prone to fall prey to the evil hacker sharks of information technology. In this paper, we provide a review of the state-of-the-art methods for privacy and analyze the representative technique for privacy preserving data mining and points out their merits and demerits. Finally the present problems and directions for future research are discussed.

A Study Survey of Privacy Preserving Data Mining

Data mining is the extraction of interesting patterns or knowledge from huge amount of data. In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. 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 methods and techniques have been developed for privacy preserving data mining. This paper provides a wide survey of different privacy preserving data mining algorithms and analyses the representative techniques for privacy preserving data mining, and points out their merits and demerits. Finally the present problems and directions for future research are discussed.

Privacy Preserving Data Mining: A Comprehensive Survey

International Journal of Computer Applications, 2017

Privacy preserving data mining has emerged due to large usage of data in organizations for extracting knowledge from data[1]. Big data uses centralized as well as distributed data and mines knowledge. Privacy preservation of data has become critical asset due to malicious users and society issues. It is very crucial nowadays to maintain balance between ensuring privacy and extracting knowledge. These areas is burning domain for researchers till now because no such research has been done that out performs all the techniques in privacy preserving data mining. Privacy preservation is classified into many categories like data modification, data distribution, data hiding and data encryption. For performance measuring, evaluation criteria like information loss, computational overhead, data utility etc are considered. Data modification techniques mainly focus on adding errors to data or results into output which degrades the accuracy of data mining algorithm. In case of critical analysis of data, crypto graphical approaches in privacy preserving data mining which has no loss of information but overhead of computation and communication have been adopted. PPDM includes homomorphic encryption, Shamir's secret sharing scheme, oblivious transfer and many other cryptography techniques.

SURVEY PAPER ON PRIVACY PRESERVING DATA MINING TECHNIQUES

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