| Title | Privacy Preserving Big Data mining using Pseudonymization and Homomorphic Encryption |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Chandrakar, Ila, Hulipalled, Vishwanath R |
| Conference Name | 2021 2nd Global Conference for Advancement in Technology (GCAT) |
| Keywords | Big Data, data privacy, homomorphic encryption, human factors, Metrics, Organizations, parallel algorithms, privacy preserving, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | Today's data is so huge so it's referred to as "Big data." Such data now exceeds petabytes, and hence businesses have begun to store it in the cloud. Because the cloud is a third party, data must be secured before being uploaded to the cloud in such a way that cloud mining may be performed on protected data, as desired by the organization. Homomorphic encryption permits mining and analysis of encrypted data, hence it is used in the proposed work to encrypt original data on the data owner's site. Since, homomorphic encryption is a complicated encryption, it takes a long time to encrypt, causing performance to suffer. So, in this paper, we used Hadoop to implement homomorphic encryption, which splits data across nodes in a Hadoop cluster to execute parallel algorithm and provides greater privacy and performance than previous approaches. It also enables for data mining in encrypted form, ensuring that the cloud never sees the original data during mining. |
| DOI | 10.1109/GCAT52182.2021.9587765 |
| Citation Key | chandrakar_privacy_2021 |
Privacy Preserving Big Data mining using Pseudonymization and Homomorphic Encryption