Wang B, Tao F, Fang X, Liu C, Liu Y, Freiheit T. Smart manufacturing and intelligent manufacturing: a comparative review. Engineering 2021; 7 (6):738-57.

Sahoo S, Lo CY. Smart manufacturing powered by recent technological advancements: a review. J Manuf Syst 2022; 64:236-50.

Suvarna M, Yap KS, Yang W, Li J, Ng YT, Wang X. Cyber-physical production systems for data-driven, decentralized, and secure manufacturing—a perspective. Engineering 2021; 7(9):1212-23.

Li J, Sisodia D, Stafford S. On the detection of smart, self-propagating internet worms. IEEE Trans Depend Secure Comput 2023; 20(4):3051-63.

Cai Z, Xiong Z, Xu H, Wang P, Li W, Pan Y. Generative adversarial networks: a survey toward private and secure applications. ACM Comput Surv 2022; 54 (6):132.

Kombrink MH, Geradts ZJMH, Worring M. Image steganography approaches and their detection strategies: a survey. ACM Comput Surv 2025; 57(2):33.

Wang H, Zhang Z, Wang T, He S, Backes M, Chen J, et al. PrivTrace:differentially private trajectory synthesis by adaptive Markov models. In:Proceedings of the 32nd USENIX Security Symposium; 2023 Aug 9-11; Anaheim, CA, USA. Red Hook: Curran Associates, Inc.; 2023. p. 1649-66.

Du L, Zhang Z, Bai S, Liu C, Ji S, Cheng P, et al. AHEAD:adaptive hierarchical decomposition for range query under local differential privacy. In:Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security; 2021 Nov 15-19; online conference. New York City: Association for Computing Machinery; 2021. p. 1266-88.

Wang X, Ishii H, Du L, Cheng P, Chen J. Privacy-preserving distributed machine learning via local randomization and ADMM perturbation. IEEE Trans Signal Process 2020; 68:4226-41.

Wang X, Ishii H, He J, Cheng P. Dynamic privacy-aware collaborative schemes for average computation: a multi-time reporting case. IEEE Trans Inf Forensics Security 2021; 16:3843-58.

Tan J, Zhong N, Qian Z, Zhang X, Li S. Deep neural network watermarking against model extraction attack. In:Proceedings of the 31st ACM International Conference on Multimedia; 2023 Oct 29-Nov 3; Ottawa, ON, Canada. New York City: Association for Computing Machinery; 2023. p. 1588-97.

Kieu T, Yang B, Guo C, Cirstea RG, Zhao Y, Song Y. Anomaly detection in time series with robust variational quasi-recurrent autoencoders. In:Proceedings of 2022 IEEE 38th International Conference on Data Engineering;2022 May 9-12; Kuala Lumpur, Malaysia. Piscataway: IEEE; 2022. p. 1342-54.

Chen D, Liao Z, Chen R, Wang H, Yu C, Zhang K, et al. Privacy-preserving anomaly detection of encrypted smart contract for blockchain-based data trading. IEEE Trans Dependable Secur Comput 2024; 21(5):4510-25.

Gao X, Ma X, Wang J, Sun Y, Li B, Ji S, et al. VeriFi: towards verifiable federated unlearning. IEEE Trans Depend Secure Comput 2024; 21(6):5720-36.

Wang X, Fang C, Yang M, Wu X, Zhang H, Cheng P. Resilient distributed classification learning against label flipping attack: an ADMM-based approach. IEEE Internet Things J 2023; 10(17):15617-31.

Carlini N, Hayes J, Nasr M, Jagielski M, Sehwag V, Tramèr F, et al. Extracting training data from diffusion models. In:Proceedings of the 32nd USENIX Security Symposium; 2023 Aug 9-11; Anaheim, CA, USA. Red Hook: Curran Associates, Inc.; 2023. p. 5253-70.

Zhang Y, Yang Q. A survey on multi-task learning. IEEE Trans Knowl Data Eng 2022; 34(12):5586-609.

Ren H, Lu W, Xiao Y, Chang X, Wang X, Dong Z, et al. Graph convolutional networks in language and vision: a survey. Knowl-Based Syst 2022; 251:109250.

Wang X, Wang Y, Yang M, Li F, Wu X, Fan L, et al. FedSiam-DA: dual-aggregated federated learning via Siamese network for non-IID data. IEEE Trans Mobile Comput 2025; 24(2):985-98.

Liu Y, Wen R, He X, Salem A, Zhang Z, Backes M, et al. ML-Doctor:holistic risk assessment of inference attacks against machine learning models. In:Proceedings of the 31st USENIX Security Symposium; 2022 Aug 10-12; Boston, MA, USA. Red Hook: Curran Associates, Inc.; 2022. p. 4525-42.

Zhu L, Liu Z, Han S. Deep leakage from gradients. In: Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, editors. Advances in neural information processing systems 32: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019); 2019 Dec 8-14; Vancouver, BC, Canada Red Hook: Curran Associates, Inc.; 2019.

Liu B, Ding M, Shaham S, Rahayu W, Farokhi F, Lin Z. When machine learning meets privacy: a survey and outlook. ACM Comput Surv 2022; 54(2):31.

Al-Rubaie M, Chang JM. Privacy-preserving machine learning: threats and solutions. IEEE Secur Priv 2019; 17(2):49-58.

Zhao F, Zhang C, Geng B. Deep multimodal data fusion. ACM Comput Surv 2024; 56(9):216.

Dwork C, Roth A. The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 2014; 9(3-4):211-407.

NasrM, Shokri R, HoumansadrA. Comprehensive privacy analysis of deep learning:passive and active white-box inference attacks against centralized and federated learning. In:Proceedings of 2019 IEEE Symposium on Security and Privacy; 2019 May 19-23; San Francisco, CA, USA. Piscataway: IEEE; 2019. p. 739-53.

Lu S, Guo L, Wang W, Zhao Z, Yue T, Liu J, et al. Collaborative training of tinylarge vision language models. In:Proceedings of the 32nd ACM International Conference on Multimedia; 2024 Oct 28-Nov 1; Melbourne, VIC, Australia. New York City: Association for Computing Machinery; 2024. P. 4928-37.

Zabihi Z, Eftekhari Moghadam AM, Rezvani MH. Reinforcement learning methods for computation offloading: a systematic review. ACM Comput Surv 2023; 56(1):1-41.

Kaur A, Noori Hoshyar A, Saikrishna V, Firmin S, Xia F. Deepfake video detection: challenges and opportunities. Artif Intell Rev 2024; 57(6):159.

Du L, Chen M, Sun M, Ji S, Cheng P, Chen J, et al. ORL-AUDITOR: dataset auditing in offline deep reinforcement learning. In:Proceedings of Network and Distributed System Security Symposium 2024; 2024 Feb 26-Mar 1; San Diego, CA, USA; 2024. p. 184.