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难点

2017

L-PPTD, L^2-PPTD1 :

  • 问题：用户端计算量太大
  • 解决思路：转移到服务器端（双不共谋服务器模型）

Effective and Efficient TD over Data Streams2 :

  • 问题：流数据
  • 解决思路：自适应更新权重

TD from Distributed Data3 :

  • 问题：大规模的数据分布在多个服务器中
  • 解决思路：本地服务器计算一部分，中心再计算

EPTD4 :

  • 问题：性能与隐私
  • 解决思路：加法同态 + 超递增序列

2018

Non-Interactive PPTD5 :

  • 问题：需要在线
  • 解决思路：双服务器模型 + GC

Encrypted CATD6 :

  • 问题： 具有隐私保护的 CATD
  • 解决思路： GC + HE (offline)

2019

Sybil-Resistant TD7 :

  • 问题： Sybil Attack
  • 解决思路： 根据陀螺仪、加速器判断是否来自同一设备

RTPT8 :

  • 问题： 实时性，大规模，用户退出
  • 解决思路： ICRH + double masking

EPTD9 :

  • 问题：双服务器模型假设不合理
  • 解决思路：double masking

RPTD-I & RPTD-II10 :

  • 问题： 可验证性
  • 解决思路： 哈希链

LPTD-I & LPTD-II11 :

  • 问题：可验证性
  • 解决思路： 哈希链

PPTDS12 :

  • 问题： 错误数据（可验证性）
  • 解决思路：

  1. C. Miao, L. Su, W. Jiang, Y. Li, and M. Tian, “A lightweight privacy-preserving truth discovery framework for mobile crowd sensing systems,” in IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, May 2017, pp. 1–9, doi: 10.1109/INFOCOM.2017.8057114. ↩

  2. T. Li, Y. Gu, X. Zhou, Q. Ma, and G. Yu, “An Effective and Efficient Truth Discovery Framework over Data Streams,” 2017, doi: 10.5441/002/edbt.2017.17. ↩

  3. Y. Wang, F. Ma, L. Su, and J. Gao, “Discovering Truths from Distributed Data,” in 2017 IEEE International Conference on Data Mining (ICDM), Nov. 2017, pp. 505–514, doi: 10.1109/ICDM.2017.60. ↩

  4. G. Xu, H. Li, C. Tan, D. Liu, Y. Dai, and K. Yang, “Achieving efficient and privacy-preserving truth discovery in crowd sensing systems,” Computers & Security, vol. 69, pp. 114–126, Aug. 2017, doi: 10.1016/j.cose.2016.11.014. ↩

  5. X. Tang, C. Wang, X. Yuan, and Q. Wang, “Non-Interactive Privacy-Preserving Truth Discovery in Crowd Sensing Applications,” in IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, Apr. 2018, pp. 1988–1996, doi: 10.1109/INFOCOM.2018.8486371. ↩

  6. Y. Zheng, H. Duan, and C. Wang, “Learning the Truth Privately and Confidently: Encrypted Confidence-Aware Truth Discovery in Mobile Crowdsensing,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 10, pp. 2475–2489, Oct. 2018, doi: 10.1109/TIFS.2018.2819134. ↩

  7. J. Lin, D. Yang, K. Wu, J. Tang, and G. Xue, “A Sybil-Resistant Truth Discovery Framework for Mobile Crowdsensing,” in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Jul. 2019, pp. 871–880, doi: 10.1109/ICDCS.2019.00091. ↩

  8. Y. Liu, S. Tang, H.-T. Wu, and X. Zhang, “RTPT: A framework for real-time privacy-preserving truth discovery on crowdsensed data streams,” Computer Networks, vol. 148, pp. 349–360, Jan. 2019, doi: 10.1016/j.comnet.2018.11.018. ↩

  9. G. Xu, H. Li, S. Liu, M. Wen, and R. Lu, “Efficient and Privacy-Preserving Truth Discovery in Mobile Crowd Sensing Systems,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3854–3865, Apr. 2019, doi: 10.1109/TVT.2019.2895834. ↩

  10. C. Zhang, L. Zhu, C. Xu, X. Liu, and K. Sharif, “Reliable and Privacy-Preserving Truth Discovery for Mobile Crowdsensing Systems,” IEEE Transactions on Dependable and Secure Computing, pp. 1–1, 2019, doi: 10.1109/TDSC.2019.2919517. ↩

  11. C. Zhang, L. Zhu, C. Xu, K. Sharif, X. Du, and M. Guizani, “LPTD: Achieving lightweight and privacy-preserving truth discovery in CIoT,” Future Generation Computer Systems, vol. 90, pp. 175–184, Jan. 2019, doi: 10.1016/j.future.2018.07.064. ↩

  12. C. Zhang, L. Zhu, C. Xu, K. Sharif, and X. Liu, “PPTDS: A privacy-preserving truth discovery scheme in crowd sensing systems,” Information Sciences, vol. 484, pp. 183–196, May 2019, doi: 10.1016/j.ins.2019.01.068. ↩