1. Cloud Data Anonymization Using Hadoop Map-Reduce Framework With Qos Evaluation and Behaviour analysis
PROJECT GUIDE:
Ms.S.Subbulakshmi
TEAM MEMBERS:
A.Mahalakshmi( ).
S.Sundareswari( ).

2. PROJECT OVERVIEW
To achieve scalability and to measure the quality of the system designed we propose an Anonymized data using Hadoop-Map reduce in cloud with QOS evaluation.
A scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the Map Reduce framework on cloud.
We do Behavioural analysis and analyse the QOS parameters such as availability, throughput, transmission delay, latency, scalability and speed.

3. EXISTING SYSTEM
Existing systems use TDS algorithms which are centralized, resulting in their inadequacy in handling large scale data sets.
Although some distributed algorithms have been proposed, they mainly focus on secure anonymization of data sets from multiple parties, rather than the scalability aspect.
This algorithm is only suitable for small amount of data sets and the level of anonymization is also low.

4. PROPOSED SYSTEM
A scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the Map Reduce framework on cloud with QOS evaluation and Behavioural analysis.
This approach splits the input data and anonymisation is applied to this small data sets, then they are merged together to get the final result.
We analyze the each and every data set sensitive field and give priority for this sensitive field.
Then we analyze QOS parameters of the system with Behavioral analysis.

5. MODULES Data Partition Anonymization Merging
Optimised Balanced Scheduling Algorithm
QOS Evaluation and Behaviour Analysis

6. OPTIMISED BALANCE SCHEDULING
Load balancing is a pre-requirement service for improving the cloud performance and for maximum utilisation of resources.
It is the process of assigning the total loads into the individual nodes of the collective system to make the resource utilisation effective and to improve the response time of the job.
This process removes the situation where some nodes are over loaded and some under loaded.

7. Selecting Hadoop Location

12. DATA PARTITION
We collect the large no of data sets. We split the large data set into small data sets.
Then we provide the random number for each data set and analyze the parameters and size of the dataset.
Based on the size of the data we partition the data into number of files and upload in the cloud.

15. DATA ANONYMISATION
In the anonymization process we are going to identify the sensitive parameters.
After identification of sensitive parameters we are going to apply anonymization to those parameters.
Then we view the parameters based on the anonymized results.
For anonymisation we use Bottom-up Generalisation approach.It hides the detailed information by creating successive layers.

22. MERGING The intermediate result of the small data sets are merged.
The MRTDS driver is used to organize the small intermediate result for merging.
The merged data sets are collected on cloud.
Anonymisation is applied again, called as specialization.

23. CONCLUSION
In this paper, we have investigated the scalability problem of large-scale data anonymization by Top-Down Specialization (TDS), and proposed a highly scalable two-phase TDS approach using Map Reduce on cloud.
The QOS parameters such as availability, throughput, transmission delay, latency, scalability and speed are also computed to compare its efficiency with the existing system.