• Jignasha Kapadiya
• Jaimeel Shah

In today’s world, privacy/anonymization of users data is of at most important. In this Study uses a clustering algorithm as a pre-process for privacy preserving methods to improve the diversity of anonymized data. T-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We review Paillier`s Encryption and application to privacy preserving computation outsourcing and secure system (e.g. Online voting). Our construction begins with a somewhat homomorphic encryption scheme that works when the function is the scheme’s own decryption function. We will show how, anonymization and encryption works together for better privacy preserving in tabular data.

K-anonymity, l-diversity, t-closeness asymmetric and symmetric

"A Review on Privacy Violations of Data Anonymization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.121-126, May - 2019, Available :http://www.jetir.org/papers/JETIRCQ06021.pdf

"A Review on Privacy Violations of Data Anonymization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp121-126, May - 2019, Available at : http://www.jetir.org/papers/JETIRCQ06021.pdf