Visible to the public Biblio

Filters: Author is Park, Seog  [Clear All Filters]
2018-09-28
Jung, Taebo, Jung, Kangsoo, Park, Sehwa, Park, Seog.  2017.  A noise parameter configuration technique to mitigate detour inference attack on differential privacy. 2017 IEEE International Conference on Big Data and Smart Computing (BigComp). :186–192.

Nowadays, data has become more important as the core resource for the information society. However, with the development of data analysis techniques, the privacy violation such as leakage of sensitive data and personal identification exposure are also increasing. Differential privacy is the technique to satisfy the requirement that any additional information should not be disclosed except information from the database itself. It is well known for protecting the privacy from arbitrary attack. However, recent research argues that there is a several ways to infer sensitive information from data although the differential privacy is applied. One of this inference method is to use the correlation between the data. In this paper, we investigate the new privacy threats using attribute correlation which are not covered by traditional studies and propose a privacy preserving technique that configures the differential privacy's noise parameter to solve this new threat. In the experiment, we show the weaknesses of traditional differential privacy method and validate that the proposed noise parameter configuration method provide a sufficient privacy protection and maintain an accuracy of data utility.

2018-05-09
Park, Sang-Hyun, Kang, Min-Suk, Yoon, So-Hye, Park, Seog.  2017.  Identical User Tracking with Behavior Pattern Analysis in Online Community. Proceedings of the Symposium on Applied Computing. :1086–1089.
The proliferation of mobile technology promotes social activities without time and space limitation. Users share information about their interests and preferences through a social network service, blog, or community. However, sensitive personal information may be exposed with the use of social activities. For example, a specific person can be identified according to exposure of personal information on the web. In this paper, we shows that a nickname that is used in an online community can be tracked by analysis of a user's behavior even though the nickname is changed to avoid identification. Unlike existing studies about user identification in a social network service, we focus on online community, which has not been extensively studied. We analyze characteristics of the online community and propose a method to track a user's nickname change to identify the user. We validate the proposed method using data collected from the online community. Results show that the proposed method can track the user's nickname change and link the old nickname with the new one.