For Project Enquiry +91 9962 067 067

A SCALABLE TWO-PHASE TOP-DOWN

SPECIALIZATION APPROACH FOR DATA

ANONYMIZATION USING MAPREDUCE ON CLOUD

ABSTRACT

Cloud computing provides massive computation power and storage capacity via utilizing a large number of commodity computers together, enabling users to deploy applications cost-effectively without heavy infrastructure investment. At present, the scale of data in many cloud applications increases tremendously in accordance with the Big Data trend, thereby making it a challenge for commonly used software tools to capture, manage, and process such large-scale data within a tolerable elapsed time. As a result, it is a challenge for existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. we propose a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud. In both phases of our approach, we deliberately design a group of innovative MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- The scale of data in many cloud applications increases tremendously in accordance with the Big Data trend. The centralized TDS approaches exploits the data structure TIPS to improve the scalability and efficiency by indexing anonymous data records and retaining statistical information in TIPS.	PROPOSED CONCEPT:- We propose top-down specialization (TDS) approach for large-scale data Anonymization. The TDS approach, offering a good tradeoff between data utility and data consistency, is widely applied for data Anonymization. We deliberately design a group of innovative MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way.
EXISTING ALGORITHM:- Centralized TDS approach.	PROPOSED ALGORITHM:-       Distributed TDS approach.
ALGORITHM DEFINITION:- There is an assumption that all data processed should fit in memory for the centralized approaches. The amount of metadata retained to maintain the statistical information and linkage information	ALGORITHM DEFINITION:- Based on MapReduce on cloud.     Meta data is lower than original data.
DRAWBACKS:- privacy  not achieved due to their insuffient in scalable. Most TDS algorithms are centralized, resulting in their inadequacy in handling large scale data sets . Less efficient using TDS. The amount of metadata retained to maintain the statistical information and linkage information of record partitions is relatively large compared with data sets.	ADVANTAGES:- Large scale of privacy obtained easily. Distributed TDS algorithms provide scalable and security to large data sets.   The scalability and efficiency of TDS can be improved significantly.   Meta data for original data was less using distributed Top down Specialization approach.