Sung-Bong Jang

Efficient multimedia big data anonymization

So far, the data anonymization approaches based on k-anonymity and l-diversity has contributed much to privacy protection from record and attributes linkage attacks. However, the existing solutions are not efficient when applied to multimedia Big Data anonymization. This paper analyzes this problem in detail in terms of the processing time, memory space, and usability, and presents two schemes to overcome such inefficiency. The first one is to reduce the processing time and space by minimizing the temporary buffer usage during anonymization process. The second is to construct an early taxonomy during the database design. The idea behind this approach is that database designers should take preliminary actions for anonymization during the early stage of a database design to alleviate the burden placed on data publishers. To evaluate the effectiveness and feasibility of these schemes, specific application tools based on the proposed approaches were implemented and experiments were conducted.

File similarity evaluation scheme for multimedia data using partial hash information

File similarity is a numerical indicator that how many duplicated data exist in target files. With this information, we can reduce storage capacity with data deduplication scheme, further it can be exploited in the digital forensic field for finding malicious software. However, measuring file similarity between files can cause a high overhead in terms of processing time and the capacity of disk storage. For this reason, in this paper, we propose a novel file similarity evaluation algorithm called PHISA (Partial Hash Information String Algorithm). To evaluate the performance of the proposed system, we compare PHISA to well-known file similarity tools. The evaluation result shows that PHISA reduces the processing time and increases the similarity evaluation accuracy.