Privacy Preservation on Big Data using Efficient Privacy Preserving Algorithm
Johnny Antony P · International Journal for Research in Applied Science and Engineering Technology · 2019
While analysing these intermediate data sets, the sensitive information can be accessed by misfeasors. Maintaining the confidentiality of this generated data set is very challengeable. Most of the existing systems uses cryptography methods for privacy preservation. The system consumes more time and cost because of the most often process of encryption and decryption of intermediate data sets which results in inefficiency and are expensive. In this article, we propose anonymization method to protect privacy of data during big data processing. In this article, we analyze a method of hiding sensitive information on big data by reconstruct a dataset according to the anonymization technique applied to clustered data. Unlike the other heuristic modification approaches, firstly, our method clusters a given dataset. Then we replace known values with unknown values in those transactions to hide a given sensitive information. Finally the sanitized dataset is generated. Our experiments show that the sensitive information can be hidden completely on the reconstructed datasets. Information leakage is problem in big data environment. Encryption of data is common method to reduce information leakage. Many cryptographic techniques are developed in past, but these techniques are much complex and time consuming. To improve search efficiency and to provide privacy preservation for big data environment Efficient Privacy Preserving (EPP) Algorithm is used in this article. EPP Algorithm is compared with encryption algorithm called Data Encryption Strategy and proved EPP algorithm performs better based on various criteria.