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Filters: Author is Sun, Yuxin  [Clear All Filters]
2021-08-31
Ge, Chonghui, Sun, Jian, Sun, Yuxin, Di, Yunlong, Zhu, Yongjin, Xie, Linfeng, Zhang, Yingzhou.  2020.  Reversible Database Watermarking Based on Random Forest and Genetic Algorithm. 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :239—247.
The advancing information technology is playing more and more important role in data mining of relational database.1 The transfer and sharing of databases cause the copyright-related security threats. Database watermarking technology can effectively solve the problem with copyright protection and traceability, which has been attracting researchers' attention. In this paper, we proposed a novel, robust and reversible database watermarking technique, named histogram shifting watermarking based on random forest and genetic algorithm (RF-GAHCSW). It greatly improves the watermark capacity by means of histogram width reduction and eliminates the impact of the prediction error attack. Meanwhile, random forest algorithm is used to select important attributes for watermark embedding, and genetic algorithm is employed to find the optimal secret key for the database grouping and determine the position of watermark embedding to improve the watermark capacity and reduce data distortion. The experimental results show that the robustness of RF-GAHCSW is greatly improved, compared with the original HSW, and the distortion has little effect on the usability of database.
2021-10-12
Sun, Yuxin, Zhang, Yingzhou, Zhu, Linlin.  2020.  An Anti-Collusion Fingerprinting based on CFF Code and RS Code. 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :56–63.
Data security is becoming more and more important in data exchange. Once the data is leaked, it will pose a great threat to the privacy and property security of users. Copyright authentication and data provenance have become an important requirement of the information security defense mechanism. In order to solve the collusion leakage of the data distributed by organization and the low efficiency of tracking the leak provenance after the data is destroyed, this paper proposes a concatenated-group digital fingerprint coding based on CFF code and Reed-solomon (RS) that can resist collusion attacks and corresponding detection algorithm. The experiments based on an asymmetric anti-collusion fingerprint protocol show that the proposed method has better performance to resist collusion attacks than similar non-grouped fingerprint coding and effectively reduces the percentage of misjudgment, which verifies the availability of the algorithm and enriches the means of organization data security audit.