| Title | Big Data Privacy Based on Differential Privacy a Hope for Big Data |
| Publication Type | Conference Paper |
| Year of Publication | 2014 |
| Authors | Shrivastva, Krishna Mohan Pd, Rizvi, M.A., Singh, Shailendra |
| Conference Name | 2014 International Conference on Computational Intelligence and Communication Networks |
| Date Published | nov |
| Keywords | anonymization, Big Data, big data privacy, data privacy, Databases, Differential privacy, Encryption, Human Behavior, human factors, Metrics, Noise, privacy, Privacy approaches, pubcrawl, Resiliency, Scalability |
| Abstract | In era of information age, due to different electronic, information & communication technology devices and process like sensors, cloud, individual archives, social networks, internet activities and enterprise data are growing exponentially. The most challenging issues are how to effectively manage these large and different type of data. Big data is one of the term named for this large and different type of data. Due to its extraordinary scale, privacy and security is one of the critical challenge of big data. At the every stage of managing the big data there are chances that privacy may be disclose. Many techniques have been suggested and implemented for privacy preservation of large data set like anonymization based, encryption based and others but unfortunately due to different characteristic (large volume, high speed, and unstructured data) of big data all these techniques are not fully suitable. In this paper we have deeply analyzed, discussed and suggested how an existing approach "differential privacy" is suitable for big data. Initially we have discussed about differential privacy and later analyze how it is suitable for big data. |
| DOI | 10.1109/CICN.2014.167 |
| Citation Key | shrivastva_big_2014 |
Big Data Privacy Based on Differential Privacy a Hope for Big Data