• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Wang Xiaoming, Yao Guoxiang, and Liao Zhiwei. Cryptanalysis and Modification of a Traitor Tracing Scheme[J]. Journal of Computer Research and Development, 2013, 50(10): 2092-2099.
Citation: Wang Xiaoming, Yao Guoxiang, and Liao Zhiwei. Cryptanalysis and Modification of a Traitor Tracing Scheme[J]. Journal of Computer Research and Development, 2013, 50(10): 2092-2099.

Cryptanalysis and Modification of a Traitor Tracing Scheme

More Information
  • Published Date: October 14, 2013
  • Recently, Wang et al. proposed a traitor tracing scheme based on bilinear map. They claimed that their scheme cloud achieve full collusion resistance, full revocation, full recoverability and black-box traceability, which is efficient in terms of the translation overhead and storage overhead in comparison with the previously proposed schemes. In this paper, we analyze their scheme and show that their scheme does not achieve full revocation. Then we modify their scheme and propose a new traitor tracing scheme based on bilinear map. In this scheme, we employ the polynomial function and the filter function as the basic means of constructing the traitor tracing procedures in order to minimize the storage, computational and communication costs. More importantly, when traitors are found, this scheme can safely revoke their private keys without updating the private keys of other receivers and deter the revoked users to recover the decryption key. Therefore, it can achieve full revocation, and thus overcomes the weakness in Wang et al.' scheme. The security of the proposed scheme is based on the difficult problems of solving bilinear discrete logarithm problem and decision Diffie-Hellman problem. The proof of security and analysis of performance show that the proposed scheme is secure and able to achieve full collusion resistance, full recoverability, black-box traceability and full revocation. Moreover, the scheme is better than Wang et al's scheme in terms of the storage, computation and communication costs.
  • Related Articles

    [1]Wu Jinjin, Liu Quan, Chen Song, Yan Yan. Averaged Weighted Double Deep Q-Network[J]. Journal of Computer Research and Development, 2020, 57(3): 576-589. DOI: 10.7544/issn1000-1239.2020.20190159
    [2]Bai Chenjia, Liu Peng, Zhao Wei, Tang Xianglong. Active Sampling for Deep Q-Learning Based on TD-error Adaptive Correction[J]. Journal of Computer Research and Development, 2019, 56(2): 262-280. DOI: 10.7544/issn1000-1239.2019.20170812
    [3]Zhu Fei, Wu Wen, Liu Quan, Fu Yuchen. A Deep Q-Network Method Based on Upper Confidence Bound Experience Sampling[J]. Journal of Computer Research and Development, 2018, 55(8): 1694-1705. DOI: 10.7544/issn1000-1239.2018.20180148
    [4]Chen Tieming, Yang Yimin, Chen Bo. Maldetect: An Android Malware Detection System Based on Abstraction of Dalvik Instructions[J]. Journal of Computer Research and Development, 2016, 53(10): 2299-2306. DOI: 10.7544/issn1000-1239.2016.20160348
    [5]Fu Ning, Zhou Xingshe, Zhan Tao. QPi: A Calculus to Enforce Trustworthiness Requirements[J]. Journal of Computer Research and Development, 2011, 48(11): 2120-2130.
    [6]Liu Tao, He Yanxiang, Xiong Qi. A Q-Learning Based Real-Time Mitigating Mechanism against LDoS Attack and Its Modeling and Simulation with CPN[J]. Journal of Computer Research and Development, 2011, 48(3): 432-439.
    [7]Zhao Ming, Luo Jizhou, Li Jianzhong, and Gao Hong. XCluster: A Cluster-Based Queriable Multi-Document XML Compression Method[J]. Journal of Computer Research and Development, 2010, 47(5): 804-814.
    [8]Deng Shanshan, Sun yi, Zhang Lisheng, Mo Zhifeng, Xie Yingke. Design of HighSpeed FFT Processor for Length N=q×2\+m[J]. Journal of Computer Research and Development, 2008, 45(8): 1430-1438.
    [9]Han Jingyu, Xu Lizhen, and Dong Yisheng. An Approach for Detecting Similar Duplicate Records of Massive Data[J]. Journal of Computer Research and Development, 2005, 42(12): 2206-2212.
    [10]Li Ronglu, Wang Jianhui, Chen Xiaoyun, Tao Xiaopeng, and Hu Yunfa. Using Maximum Entropy Model for Chinese Text Categorization[J]. Journal of Computer Research and Development, 2005, 42(1): 94-101.

Catalog

    Article views (718) PDF downloads (528) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return