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Data mining is the process of extracting interesting patterns or knowledge from huge amount of data. In recent years, there has been a tremendous growth in the amount of personal data that can be collected and analyzed by the organizations. Organizations such as credit card companies, real estate companies and hospitals collect and hold large volumes of data for their research purposes. E.g. National Institute of health. When these organizations publish data containing a lot of sensitive information. The importance of sharing data for research and knowledge discovery has been well-recognized. However, sharing data that contains sensitive personal information, such as insurance data, medical record, etc across organization boundaries can raise serious privacy concerns. There is a need to preserve the privacy of the individuals in data set. K-anonymity is one of the easy and efficient techniques to achieve privacy in many data publishing applications. In k-anonymity, all tuples of releasing database are generalized to make it anonymize which lead to data utility reduction and more information loss of publishing table. Sensitive attribute based anonymity method is very useful in preserving the privacy of individuals in organization’s publication of data. It reduces information loss to the researchers by providing sensitive levels. This method also avoids Homogeneity attack and Background attacks.

Abstract: The main goal of this research is to develop and implement data privacy appropriate technique that fit with E-health system. Privacy preserving data is to develop methods without increasing the risk of misuse of the data used to generate those methods. A number of effective methods for privacy preserving data mining have been proposed. Privacy preservation of sensitive information is a key factor of current era. This study concludes a comprehensives analysis against trendy privacy control. Anonmization offers more privacy options rather to other privacy preservation techniques (Randomization, Encryption, and Sanitization). However, Anonmization itself contains several techniques that require concluding best one. According to the presented analysis there is close competition among K- Anonymity, L-Diversity, T-Closeness, P-Sensitive and M-invariance. All these Anonmization methods offer resistance against prominent attacks like homogeneity and background. I conducted the analytical comparative analysis to select optimal Anonymization methods. Finally, the resulting algorithm has been implemented for both K-anonymity and L-Diversity algorithms with figures, charts and diagrams.

E. Shmueli and T. Tassa contributed equally to this work. Privacy Preserving Data Publishing (PPDP) is a research field that deals with the development of methods to enable publishing of data while minimizing distortion, for maintaining usability on one hand, and respecting privacy on the other hand. Sequential release is a scenario of data publishing where multiple releases of the same underlying table are published over a period of time. A violation of privacy, in this case, may emerge from any one of the releases, or as a result of joining information from different releases. Similarly to [37], our privacy definitions limit the ability of an adversary who combines information from all releases, to link values of the quasi-identifiers to sensitive values. We extend the framework that was considered in [37] in three ways: We allow a greater number of releases, we consider the more flexible local recoding model of “cell generalization ” (as opposed to the global recoding model of “cut generalization ” in [37]), and we include the case where records may be added to the underlying table from time to time. Our extension of the framework requires also to modify the manner in which privacy is evaluated. We show

Abstract--Generally mining of data is a well-known technique for automatically and intelligently extracting information or knowledge from a large amount of data, however, it can also disclosure sensitive information about individuals compromising the individual’s right to privacy.It is a process to extract the implicit information;knowledge which is potentially useful and people do not know in advance, and this extraction is from the mass, incomplete, noisy, fuzzy and random data.Therefore, privacy preserving datamining has becoming an increasingly important field of research. Now aday, Data mining is emerging area to extract implicit and useful knowledgeand also recognized as an important technology for businesses internationally and locally. 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.Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization”. Undoubtedly, research in data mining will continue and even increase over coming decades. Hence this paper sketches vision of the future workto done inarea of data mining. This paper elaborate various topics (starting from the classic definition of “datamining ” and its basic terms)included variousfuture challenges and issues in data mining which is important to do further more research in this emerging field.

Abstract — 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.