Page 326 - 《软件学报》2026年第6期
P. 326
陈毅飞 等: 安全可信的数据要素流通综述 2645
and Practice of Consistency for Distributed Data (PaPoC). Heraklion: ACM, 2020. 8. [doi: 10.1145/3380787.3393681]
[125] Hendrycks D, Zhao K, Basart S, Steinhardt J, Song D. Natural adversarial examples. In: Proc. of the 2021 IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 15257–15266. [doi: 10.1109/CVPR46437.2021.01501]
[126] Nohara Y, Matsumoto K, Soejima H, Nakashima N. Explanation of machine learning models using Shapley additive explanation and
application for real data in hospital. Computer Methods and Programs in Biomedicine, 2022, 214: 106584. [doi: 10.1016/j.cmpb.2021.
106584]
[127] Levi M, Kontorovich A. Splitting the difference on adversarial training. In: Proc. of the 33rd USENIX Security Symp. Philadelphia:
USENIX Association, 2024. 3639–3656.
[128] Sun HC, Bai TH, Li J, Zhang HY. zkDL: Efficient zero-knowledge proofs of deep learning training. IEEE Trans. on Information
Forensics and Security, 2025, 20: 914–927. [doi: 10.1109/TIFS.2024.3520863]
[129] Fiore D, Tucker I. Efficient zero-knowledge proofs on signed data with applications to verifiable computation on data streams. In: Proc.
of the 2022 ACM SIGSAC Conf. on Computer and Communications Security. Los Angeles: ACM, 2022. 1067–1080. [doi: 10.1145/
3548606.3560630]
[130] Chen HB, Chen HH, Sun MS, Li K, Chen ZF, Wang XF. A verified confidential computing as a service framework for privacy
preservation. In: Proc. of the 32nd USENIX Security Symp. Anaheim: USENIX Association, 2023. 4733–4750.
[131] Zhang S, Zhang Y, Zhang W, Huang G. Method of data service integrity verification based on remote attestation. Ruan Jian Xue
Bao/Journal of Software, 2024, 35(11): 4949–4972 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/7001.htm [doi:
10.13328/j.cnki.jos.007001]
[132] Peng Z, Xu JL, Chu XW, Gao S, Yao Y, Gu R, Tang YZ. VFChain: Enabling verifiable and auditable federated learning via blockchain
systems. IEEE Trans. on Network Science and Engineering, 2022, 9(1): 173–186. [doi: 10.1109/TNSE.2021.3050781]
[133] Zhang ZJ, Lu X, Li M, An JC, Yu Y, Yin H, Zhu LH, Liu Y, Liu JM, Khoussainov B. A blockchain-based privacy-preserving scheme
for sealed-bid auction. IEEE Trans. on Dependable and Secure Computing, 2024, 21(5): 4668–4683. [doi: 10.1109/TDSC.2024.
3353540]
[134] Zhang MW, Fan TY, Wang YZ, Zhu TQ, Yang B. Multiparty chained adaptor signatures based on the SM2 algorithm. Scientia Sinica
Informationis, 2025, 55(6): 1406–1427 (in Chinese with English abstract). [doi: 10.1360/SSI-2025-0009]
[135] Wan ZG, Zhou Y, Ren K. Zk-AuthFeed: Protecting data feed to smart contracts with authenticated zero knowledge proof. IEEE Trans.
on Dependable and Secure Computing, 2023, 20(2): 1335–1347. [doi: 10.1109/TDSC.2022.3153084]
[136] Adams C, Cain P, Pinkas D, Zuccherato R. Internet X.509 public key infrastructure time-stamp protocol (TSP). 2001. https://datatracker.
ietf.org/doc/html/rfc3161
[137] Zhang F, Maram D, Malvai H, Goldfeder S, Juels A. DECO: Liberating Web data using decentralized oracles for TLS. In: Proc. of the
2020 ACM SIGSAC Conf. on Computer and Communications Security. ACM, 2020. 1919–1938. [doi: 10.1145/3372297.3417239]
[138] Breidenbach L, Cachin C, Chan B, Coventry A, Ellis S, Juels A, Koushanfar F, Miller A, Magauran B, Moroz D, Nazarov S, Topliceanu
A, Tramèr F, Zhang F. Chainlink 2.0: Next steps in the evolution of decentralized oracle networks. Chainlink Labs, 2021, 1: 1–136.
[139] Yang XY, Wang S, Li FF, Zhang Y, Yan WY, Gai FY, Yu BQ, Feng LK, Gao Q, Li YZ. Ubiquitous verification in centralized ledger
database. In: Proc. of the 38th IEEE Int’l Conf. on Data Engineering. Kuala Lumpur: IEEE, 2022. 1808–1821. [doi: 10.1109/
ICDE53745.2022.00181]
[140] Liu ZW, Hu CQ, Xia H, Xiang T, Wang BL, Chen JJ. SPDTS: A differential privacy-based blockchain scheme for secure power data
trading. IEEE Trans. on Network and Service Management, 2022, 19(4): 5196–5207. [doi: 10.1109/TNSM.2022.3181814]
[141] Weng JS, Yao SL, Du YF, Huang JJ, Weng J, Wang C. Proof of unlearning: Definitions and instantiation. IEEE Trans. on Information
Forensics and Security, 2024, 19: 3309–3323. [doi: 10.1109/TIFS.2024.3358993]
[142] Aman MN, Basheer MH, Sikdar B. Data provenance for IoT with light weight authentication and privacy preservation. IEEE Internet of
Things Journal, 2019, 6(6): 10441–10457. [doi: 10.1109/JIOT.2019.2939286]
[143] Backes M, Grimm N, Kate A. Data lineage in malicious environments. IEEE Trans. on Dependable and Secure Computing, 2016, 13(2):
178–191. [doi: 10.1109/TDSC.2015.2399296]
[144] Sablayrolles A, Douze M, Schmid C, Jegou H. Radioactive data: Tracing through training. In: Proc. of the 37th Int’l Conf. on Machine
Learning. JMLR.org, 2020. 8326–8335.
[145] Tang MJ, Shao SS, Yang WQ, Liang YB, Yu YY, Saha B, Hyun D. SAC: A system for big data lineage tracking. In: Proc. of the 35th
IEEE Int’l Conf. on Data Engineering. Macao: IEEE, 2019. 1964–1967. [doi: 10.1109/ICDE.2019.00215]
[146] Deutch D, Frankenthal A, Gilad A, Moskovitch Y. On optimizing the trade-off between privacy and utility in data provenance. In: Proc.
of the 2021 Int’l Conf. on Management of Data. ACM, 2021. 379–391. [doi: 10.1145/3448016.3452835]

