Page 506 - 《软件学报》2025年第7期

张朋飞 等: 基于自适应剪枝的满足本地差分隐私的真值发现算法 3427

2024.3358909]
[34] Zheng ZR, Li ZT, Huang C, Long SQ, Li MS, Shen XM. Data poisoning attacks and defenses to LDP-based privacy-preserving
crowdsensing. IEEE Trans. on Dependable and Secure Computing, 2024, 21(5): 4861–4878. [doi: 10.1109/TDSC.2024.3363507]
[35] Hossain MT, Islam S, Badsha S, Shen HT. DeSMP: Differential privacy-exploited stealthy model poisoning attacks in federated learning.
In: Proc. of the 17th Int’l Conf. on Mobility, Sensing and Networking. Exeter: IEEE, 2021. 167–174. [doi: 10.1109/MSN53354.2021.
00038]
[36] Zhong YS, Wang LP. PROFL: A privacy-preserving federated learning method with stringent defense against poisoning attacks. In: Proc.
of the 27th Int’l Conf. on Computer Supported Cooperative Work in Design. Tianjin: IEEE, 2024. 260–265. [doi: 10.1109/CSCWD61410.
2024.10580526]
[37] Huang YX, Yang G, Zhou H, Dai H, Yuan D, Yu S. VPPFL: A verifiable privacy-preserving federated learning scheme against poisoning
attacks. Computers & Security, 2024, 136: 103562. [doi: 10.1016/j.cose.2023.103562]
[38] Zhang C, Zhu LH, Xu C, Liu XM, Sharif K. Reliable and privacy-preserving truth discovery for mobile crowdsensing systems. IEEE
Trans. on Dependable and Secure Computing, 2021, 18(3): 1245–1260. [doi: 10.1109/TDSC.2019.2919517]
[39] Xue KP, Zhu B, Yang QY, Gai N, Wei DSL, Yu NH. InPPTD: A lightweight incentive-based privacy-preserving truth discovery for
crowdsensing systems. IEEE Internet of Things Journal, 2021, 8(6): 4305–4316. [doi: 10.1109/JIOT.2020.3029294]
[40] Zhao JC, Zhu B, Li J, Yuan SX, Xue KP, Zhang XC. Privacy-preserving truth discovery with outlier detection in mobile crowdsensing
systems. In: Proc. of the 2022 IEEE Global Communications Conf. Rio de Janeiro: IEEE, 2022. 4352–4357. [doi: 10.1109/
GLOBECOM48099.2022.10000662]
[41] Wu HQ, Wang LM, Cheng K, Yang DJ, Tang J, Xue GL. Privacy-enhanced and practical truth discovery in two-server mobile
crowdsensing. IEEE Trans. on Network Science and Engineering, 2022, 9(3): 1740–1755. [doi: 10.1109/TNSE.2022.3151228]
[42] Tang JC, Fu SJ, Liu XM, Luo YC, Xu M. Achieving privacy-preserving and lightweight truth discovery in mobile crowdsensing. IEEE
Trans. on Knowledge and Data Engineering, 2022, 34(11): 5140–5153. [doi: 10.1109/TKDE.2021.3054409]
[43] Zhang C, Zhao MY, Zhu LH, Wu T, Liu XM. Enabling efficient and strong privacy-preserving truth discovery in mobile crowdsensing.
IEEE Trans. on Information Forensics and Security, 2022, 17: 3569–3581. [doi: 10.1109/TIFS.2022.3207905]
[44] Liu XT, Zhou SW, Zhang W, Dong T, Li KQ. Privacy-preserving truth discovery for collaborative-cloud encryption in mobile
crowdsensing. IEEE Systems Journal, 2023, 17(3): 4990–5001. [doi: 10.1109/JSYST.2023.3274812]
[45] Xiong P, Li GR, Liu HZ, Hu YY, Jin DW. Decentralized privacy-preserving truth discovery for crowd sensing. Information Sciences,
2023, 632: 730–741. [doi: 10.1016/j.ins.2023.03.046]
[46] Liu R, Pan JP. Lightweight privacy-preserving truth discovery for vehicular air quality monitoring. Digital Communications and
Networks, 2023, 9(1): 280–291. [doi: 10.1016/j.dcan.2022.03.021]
[47] Li YL, Miao CL, Su L, Gao J, Li Q, Ding BL, Qin Z, Ren K. An efficient two-layer mechanism for privacy-preserving truth discovery.
In: Proc. of the 24th ACM SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining. London: ACM, 2018. 1705–1714. [doi: 10.
1145/3219819.3219998]
[48] Wang D, Xu JH. Inferring ground truth from crowdsourced data under local attribute differential privacy. Theoretical Computer Science,
2021, 865: 85–98. [doi: 10.1016/j.tcs.2021.02.039]

附中文参考文献:
[1] 熊金波, 毕仁万, 田有亮, 刘西蒙, 马建峰. 移动群智感知安全与隐私: 模型、进展与趋势. 计算机学报, 2021, 44(9): 1949–1966. [doi:
10.11897/SP.J.1016.2021.01949]
[2] 童咏昕, 袁野, 成雨蓉, 陈雷, 王国仁. 时空众包数据管理技术研究综述. 软件学报, 2017, 28(1): 35–58. http://www.jos.org.cn/1000-
9825/5140.htm [doi: 10.13328/j.cnki.jos.005140]
[3] 柴成亮, 李国良, 赵天宇, 骆昱宇, 于明鹤. 众包数据库综述. 计算机学报, 2020, 43(5): 948–972. [doi: 10.11897/SP.J.1016.2020.00948]
[4] 冯剑红, 李国良, 冯建华. 众包技术研究综述. 计算机学报, 2015, 38(9): 1713–1726. [doi: 10.11897/SP.J.1016.2015.01713]
[5] 纪守领, 杜天宇, 李进锋, 沈超, 李博. 机器学习模型安全与隐私研究综述. 软件学报, 2021, 32(1): 41–67. http://www.jos.org.cn/1000-
9825/6131.htm [doi: 10.13328/j.cnki.jos.006131]
[8] 曾菊儒, 陈红, 彭辉, 吴垚, 李翠平, 王珊. 参与式感知隐私保护技术. 计算机学报, 2016, 39(3): 595–614. [doi: 10.11897/SP.J.1016.
2016.00595]
[10] 叶青青, 孟小峰, 朱敏杰, 霍峥. 本地化差分隐私研究综述. 软件学报, 2018, 29(7): 1981–2005. http://www.jos.org.cn/1000-9825/5364.
htm [doi: 10.13328/j.cnki.jos.005364]