Scalable l-Diversity

Abstract

Privacy preserving data publishing is one of the most demanding research areas in the recent few years. There are more than billions of devices capable to collect the data from various sources. To preserve the privacy while publishing data, algorithms for equivalence class generation and scalable anonymization with k-anonymity and l-diversity using MapReduce programming paradigm are proposed in this article. Equivalence class generation algorithms divide the datasets into equivalence classes for Scalable k-Anonymity (SKA) and Scalable l-Diversity (SLD) separately. These equivalence classes are finally fed to the anonymization algorithm that calculates the Gross Cost Penalty (GCP) for the complete dataset. The value of GCP gives information loss in input dataset after anonymization.

A Classification of non-Cryptographic Anonymization Techniques Ensuring Privacy in Big Data

“…Approaches Techniques Integrity Confidentiality Credibility Generalization K-anonymity based on generalization [22], [23], [24], [25] No No Yes L-diversity [2], [14], [23], [25] Yes No Yes T-closeness [2], [14], [26] Yes Yes Yes Randomization K-anonymity based on suppression [20], [24], [27] No No Yes…”

“…Whereas, sensitive attributes include confidential information belonging to a specific individual, these attributes need more protection compared to QI ones [23], [37]. The k-anonymity is achieved when all the records belonging to a set of QI attributes cannot be distinguished from at least k−1 other records in the data set [23], [25]. Moreover, every record in a k-anonymized data set has a maximum probability 1/k of being identified [25].…”

“…The k-anonymity is achieved when all the records belonging to a set of QI attributes cannot be distinguished from at least k−1 other records in the data set [23], [25]. Moreover, every record in a k-anonymized data set has a maximum probability 1/k of being identified [25]. In addition, the confidentiality of the published data is better ensured when the value of the threshold k is high enough [17], [24].…”