| Title | Data Security and Privacy Preserving with Augmented Homomorphic Re-Encryption Decryption (AHRED) Algorithm in Big Data Analytics |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Shoba, V., Parameswari, R. |
| Conference Name | 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) |
| Date Published | sep |
| Keywords | Augmented Homomorphic Re-Encryption Decryption (AHRED), Big Data, Ciphers, data privacy, Encryption, filtering algorithms, homomorphic encryption, human factors, Laplace equations, Laplacian Noise Filter, Metrics, Paillier, Partially Homomorphic Re-Encryption Decryption (PHRED), privacy, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | The process of Big data storage has become challenging due to the expansion of extensive data; data providers will offer encrypted data and upload to Big data. However, the data exchange mechanism is unable to accommodate encrypted data. Particularly when a large number of users share the scalable data, the scalability becomes extremely limited. Using a contemporary privacy protection system to solve this issue and ensure the security of encrypted data, as well as partially homomorphic re-encryption and decryption (PHRED). This scheme has the flexibility to share data by ensuring user's privacy with partially trusted Big Data. It can access to strong unforgeable scheme it make the transmuted cipher text have public and private key verification combined identity based Augmented Homomorphic Re Encryption Decryption(AHRED) on paillier crypto System with Laplacian noise filter the performance of the data provider for privacy preserving big data. |
| DOI | 10.1109/ICIRCA51532.2021.9544802 |
| Citation Key | shoba_data_2021 |
Data Security and Privacy Preserving with Augmented Homomorphic Re-Encryption Decryption (AHRED) Algorithm in Big Data Analytics