International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 8, August 2014) 386 A Review on Various Privacy Preserving Techniuqes & Classifications Algorithms Ankita Jain 1 , Shubha Dubey 2 , Anurag Jain 3 Abstract— Privacy preserving data mining is one of the most demanding research areas within the data mining community. In many cases, multiple parties may wish to share aggregate private data without disclosing any private information at user side. Over the last few years this has naturally lead to a growing interest in security or privacy issues in data mining. More precisely, it became clear that discovering knowledge through a combination of different databases raises important security issues. New dimension of structure Trust (MLT) poses new challenges for perturbation- based PPDM. In distinction to the single-level trust situation wherever just one rattled copy is released, currently multiple otherwise rattled copies of the same knowledge are offered to knowledge miners at completely different sure levels. The a lot of sure an information manual labourer is, the less rattled copy it will access; it's going to even have access to the rattled copies offered at lower trust levels. In this paper we are presenting some techniques to overcome problems related with privacy preservation and multi-level trust. Keywords— Privacy Preservation Data Mining, Multi- Level Trust, PPDM, Perturbation . I. INTRODUCTION The multilevel trust (MLT) facing the new problems for privacy preservation that is based on perturbation PPDM. While talking about scenario of single level trust where only single perturbed copy is released, but now multiple differently perturbed copies of the similar data are available to data miners at different trusted levels. As far as most trusted data miner is concerned the less perturbed copy it can access; it may also have access to the perturbed copies available at lower trust levels. Likewise a data miner could access multiple perturbed copies through various other means [1]. Data Mining Data mining is a recent emerging field with the supports of database, statistics and artificial intelligence. This helped to many organizations to gather huge amount of useful data or information. But generally gathering of useful data is quite difficult; hence knowledge extraction is also a big challenge in this respect. Data mining also known as knowledge discover, it means useful information can be retrieved by existing stored data. Figure 1 Architecture of the Data Mining Process This can also introduced new concepts and algorithms such as association rule learning. It is also best suited for known machine- learning algorithms like inductive-rule learning to the setting where very large databases are involved [2]. Confidentiality is a common problem in data mining. The privacy is the great need of organization just because of sensible data storage as well as sharing. This sharing may lead mutual gain. A key utility of large databases today is research, whether it will be scientific or economic and market oriented. Increasing privacy and security consciousness has lead to increased research and development of methods that compute useful information in a secure manner. Companies could exchange information to boost productivity, but are prevented by fear of being exploited by competitors or antitrust concerns [3]. 1.1 Privacy Preservation of Data Streams A new topic within the space of privacy preserving data processing is that of information streams, within which knowledge grows speedily at a limitless rate. In such cases, the matter of privacy-preservation is kind of difficult since the info is being free incrementally [4].