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An Efficient Mapreduce Approach Using on Cloud for Data Anonymization
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sirisha, Tuvva. Ujwala, B. Murthy, G. Vishnu |
| Copyright Year | 2016 |
| Abstract | IJPRES Abstract— A large number of cloud services need users to share private data like electronic health records for data analysis or mining, transfer privacy. Anonymizing data sets via generalization to satisfy certain privacy needs like k-anonymity may be a wide used class of privacy conserving techniques. At present, the scale of data in several cloud applications will increase enormously in accordance with the massive data trend, thereby making it a challenge for usually used software tools to capture, manage, and method such large-scale data among a tolerable period of time. As a result, it is a challenge for existing anonymization approaches to attain privacy preservation on privacy-sensitive largescale data sets because of their insufficiency of scalability. During this paper, we tend to propose a (TDS) scalable two-phase top-down specialization method to anonymize large-scale data sets using the Map-Reduce framework on cloud. In each phases of our approach, we tend to deliberately design a bunch of innovative Map-Reduce jobs to concretely accomplish the specialization computation in a very extremely scalable method. Experimental analysis results demonstrate that with our approach, the measurability and efficiency of TDS can be significantly improved over existing approaches. We tend to propose a special approach for securing data within the cloud using offensive decoy technology. We tend to monitor data access within the cloud and detect abnormal data access patterns. Once unauthorized access is suspected and so verified exploitation challenge queries, we tend to launch a disinformation attack by returning massive amounts of decoy data to the attacker and protects data adjacent to the misuse of the user’s authentic data. Experiments conducted in a native file setting give evidence that this approach could provide new levels of user data security in a very Cloud environment. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijpres.com/pdf28/41.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
