Towards Practical k-Anonymization: Correlation-based Construction of Generalization Hierarchy

Abstract

The privacy of individuals included in the datasets must be preserved when sensitive datasets are published. Anonymization algorithms such as k-anonymization have been proposed in order to reduce the risk of individuals in the dataset being identified. k-anonymization is the most common technique of modifying attribute values in a dataset until at least k identical records are generated. There are many algorithms that can be used to achieve k-anonymity. However, existing algorithms have the problem of information loss due to a tradeoff between data quality and anonymity. In this paper, we propose a novel method of constructing a generalization hierarchy for k anonymization algorithms. Our method analyses the correlation between attributes and generates an optimal hierarchy according to the correlation. The effect of the proposed scheme has been verified using the actual data: the average of k of the datasets is 83.14, and it is around 1/3 of the value obtained by conventional methods.

Steps for Security and Privacy Protection in NLP-based Marking Systems

“…Tomoaki et al proposed a k-anonymization algorithm to anonymize the data set by analyzing the correlation between attributes and generates an optimal hierarchy based on this correlation. This method thus guarantees the protection of privacy [7].…”

“…Secondly, data confidentiality must be ensured by setting up an access control system to prevent intruders from gaining access to information in the system. In addition, we explore the importance of privacy-protecting techniques in improving the confidentiality of exam data, such as anonymization and pseudonymization [7]. As this type of system is often exposed to brute force attack in order to make the services unavailable, we suggest to set up decentralized architecture [8] and dispatch the services in multiple nodes.…”