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A Differentially Private Query Processing Mechanism using a Batch Strategy within a Limited Privacy Budget
Minsuc Kang, Kangsoo Jung, Seog Park
http://doi.org/10.5626/JOK.2018.45.7.708
A differential privacy has the advantage of being able to protect information regardless of the attacker’s prior knowledge. However, it has a disadvantage in that each query consumes privacy budget. The larger the privacy budget applied to the query, the more accurate are the query results. However it increases the privacy budget consumption and creates a limitation in the query processing limitation. On the other hand, if the privacy budget allocated to each query is too small, the noise becomes too much. This causes the query result to become inaccurate, and this, in turn causes the data utility to deteriorate. In this paper, we propose a batch strategy that reorders differentially private query processing in interactive environment. The proposed technique uses less privacy budget while it guarantees the data utility.
A Method for Identifying Nicknames of a User based on User Behavior Patterns in an Online Community
http://doi.org/10.5626/JOK.2018.45.2.165
An online community is a virtual group whose members share their interests and hobbies anonymously with nicknames unlike Social Network Services. However, there are malicious user problems such as users who write offensive contents and there may exist data fragmentation problems in which the data of the same user exists in different nicknames. In addition, nicknames are frequently changed in the online community, so it is difficult to identify them. Therefore, in this paper, to remedy these problems we propose a behavior pattern feature vectors for users considering online community characteristics, propose a new implicit behavior pattern called relationship pattern, and identify the nickname of the same user based on Random Forest classifier. Also, Experimental results with the collected real world online community data demonstrate that the proposed behavior pattern and classifier can identify the same users at a meaningful level.
Exploiting Friend’s Username to De-anonymize Users across Heterogeneous Social Networking Sites
Nowadays, social networking sites (SNSs), such as Twitter, LinkedIn, and Tumblr, are coming into the forefront, due to the growth in the number of users. While users voluntarily provide their information in SNSs, privacy leakages resulting from the use of SNSs is becoming a problem owing to the evolution of large data processing techniques and the raising awareness of privacy. In order to solve this problem, the studies on protecting privacy on SNSs, based on graph and machine learning, have been conducted. However, examples of privacy leakages resulting from the advent of a new SNS are consistently being uncovered. In this paper, we propose a technique enabling a user to detect privacy leakages beforehand in the case where the service provider or third-party application developer threatens the SNS user’s privacy maliciously.
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