Page 505 - 《软件学报》2025年第7期

3426 软件学报 2025 年第 36 卷第 7 期

(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5364.htm [doi: 10.13328/j.cnki.jos.005364]
[11] Erlingsson Ú, Pihur V, Korolova A. RAPPOR: Randomized aggregatable privacy-preserving ordinal response. In: Proc. of the 2014 ACM
SIGSAC Conf. on Computer and Communications Security. Scottsdale: ACM, 2014. 1054–1067. [doi: 10.1145/2660267.2660348]
[12] Tang J, Korolova A, Bai XL, Wang XQ, Wang XF. Privacy loss in Apple’s implementation of differential privacy on MacOS 10.12.
arXiv:1709.02753, 2017.
[13] Ding BL, Kulkarni J, Yekhanin S. Collecting telemetry data privately. In: Proc. of the 31st Int’l Conf. on Neural Information Processing
Systems. Long Beach: Curran Associates Inc., 2017. 3574–3583.
[14] Husain H, Balle B, Cranko Z, Nock R. Local differential privacy for sampling. In: Proc. of the 23rd Int’l Conf. on Artificial Intelligence
and Statistics. Palermo: PMLR, 2020. 3404–3413.
[15] Lin DJ, Wu YD, Gan WS. Sybil-resistant truth discovery in crowdsourcing by exploiting the long-tail effect. In: Proc. of the 21st IEEE
Int’l Conf. on Trust, Security and Privacy in Computing and Communications. Wuhan: IEEE, 2022. 1545–1550. [doi: 10.1109/
TrustCom56396.2022.00221]
[16] Xu F, Sheng VS, Wang MW. A unified perspective for disinformation detection and truth discovery in social sensing: A survey. ACM
Computing Surveys (CSUR), 2023, 55(1): 6. [doi: 10.1145/3477138]
[17] Wang D, Kaplan L, Abdelzaher TF. Maximum likelihood analysis of conflicting observations in social sensing. ACM Trans. on Sensor
Networks (TOSN), 2014, 10(2): 30. [doi: 10.1145/2530289]
[18] Li HW, Zhao B, Fuxman A. The wisdom of minority: Discovering and targeting the right group of workers for crowdsourcing. In: Proc.
of the 23rd Int’l Conf. on World Wide Web. Seoul: ACM, 2014. 165–176. [doi: 10.1145/2566486.2568033]
[19] Zhang D, Wang D, Vance N, Zhang Y, Mike S. On scalable and robust truth discovery in big data social media sensing applications.
IEEE Trans. on Big Data, 2019, 5(2): 195–208. [doi: 10.1109/TBDATA.2018.2824812]
[20] Xu C, Jia Y, Zhu LH, Zhang C, Jin GX, Sharif K. TDFL: Truth discovery based Byzantine robust federated learning. IEEE Trans. on
Parallel and Distributed Systems, 2022, 33(12): 4835–4848. [doi: 10.1109/TPDS.2022.3205714]
[21] Zhang HN, Li MH, Sun YB, Qu GQ. Robust truth discovery against multi-round data poisoning attacks. In: Proc. of the 17th Int’l Conf.
on Wireless Algorithms, Systems, and Applications. Dalian: Springer, 2022. 258–270. [doi: 10.1007/978-3-031-19208-1_22]
[22] Sun HP, Dong BX, Wang H, Yu T, Qin Z. Truth inference on sparse crowdsourcing data with local differential privacy. In: Proc. of the
2018 IEEE Int’l Conf. on Big Data. Seattle: IEEE, 2018. 488–497. [doi: 10.1109/BigData.2018.8622635]
[23] Li YL, Xiao HP, Qin Z, Miao CL, Su L, Gao J, Ren K, Ding BL. Towards differentially private truth discovery for crowd sensing
systems. In: Proc. of the 40th Int’l Conf. on Distributed Computing Systems. Singapore: IEEE, 2020. 1156–1166. [doi: 10.1109/
ICDCS47774.2020.00037]
[24] Sun P, Wang ZB, Feng YH, Wu LT, Li YJ, Qi HR, Wang Z. Towards personalized privacy-preserving incentive for truth discovery in
crowdsourced binary-choice question answering. In: Proc. of the 39th IEEE Conf. on Computer Communications. Toronto: IEEE, 2020.
1133–1142. [doi: 10.1109/INFOCOM41043.2020.9155429]
[25] Sun P, Wang ZB, Wu LT, Feng YH, Pang XY, Qi HR, Wang Z. Towards personalized privacy-preserving incentive for truth discovery in
mobile crowdsensing systems. IEEE Trans. on Mobile Computing, 2022, 21(1): 352–365. [doi: 10.1109/TMC.2020.3003673]
[26] Zhang PF, Cheng X, Su S, Zhu BY. PrivTDSI: A local differentially private approach for truth discovery via sampling and inference.
IEEE Trans. on Big Data, 2023, 9(2): 471–484. [doi: 10.1109/TBDATA.2022.3186175]
[27] Zhang PF, Cheng X, Su S, Wang N. Effective truth discovery under local differential privacy by leveraging noise-aware probabilistic
estimation and fusion. Knowledge-based Systems, 2023, 261: 110213. [doi: 10.1016/j.knosys.2022.110213]
[28] Cao XY, Jia JY, Gong NZ. Data poisoning attacks to local differential privacy protocols. In: Proc. of the 30th USENIX Security Symp.
USENIX, 2019. 947–964.
[29] Cheu A, Smith A, Ullman J. Manipulation attacks in local differential privacy. In: Proc. of the 2021 IEEE Symp. on Security and Privacy.
San Francisco: IEEE, 2021. 883–900. [doi: 10.1109/SP40001.2021.00001]
[30] Li ZT, Zheng ZR, Guo SM, Guo B, Xiao F, Ren K. Disguised as privacy: Data poisoning attacks against differentially private
crowdsensing systems. IEEE Trans. on Mobile Computing, 2023, 22(9): 5155–5169. [doi: 10.1109/TMC.2022.3173642]
[31] Zhang HN, Li MH. Multi-round data poisoning attack and defense against truth discovery in crowdsensing systems. In: Proc. of the 23rd
IEEE Int’l Conf. on Mobile Data Management. Paphos: IEEE, 2022. 109–118. [doi: 10.1109/MDM55031.2022.00036]
[32] Song SR, Xu L, Zhu LH. Efficient defenses against output poisoning attacks on local differential privacy. IEEE Trans. on Information
Forensics and Security, 2023, 18: 5506–5521. [doi: 10.1109/TIFS.2023.3305873]
[33] Huang K, Ouyang GY, Ye QQ, Hu HB, Zheng BL, Zhao X, Zhang RY, Zhou XF. LDPGuard: Defenses against data poisoning attacks to
local differential privacy protocols. IEEE Trans. on Knowledge and Data Engineering, 2024, 36(7): 3195–3209. [doi: 10.1109/TKDE.