1. Voigt, P., Von dem Bussche, A.: The EU general data protection regulation (GDPR. A Practical Guide, 1st Ed., Cham: Springer International Publishing10(3152676), 10–5555 (2017)

2. Hoofnagle, C.J., van der Sloot, B., Borgesius, F.Z.: The european union general data protection regulation: what it is and what it means. Inform. Commun. Technol. Law28(1), 65–98 (2019)

   Article  Google Scholar 

3. Chik, W.B.: The singapore personal data protection act and an assessment of future trends in data privacy reform. Comput. Law Secur. Rev.29(5), 554–575 (2013)

   Article  Google Scholar 

4. Shatz, S., Chylik, S.E.: The california consumer privacy act of 2018: A sea change in the protection of california consumers. The Business Lawyer75 (2020)

5. Hu, H., Salcic, Z., Sun, L., Dobbie, G., Yu, P.S., Zhang, X.: Membership inference attacks on machine learning: A survey. ACM Comput. Surv. (CSUR)54(11s), 1–37 (2022)

   Article  Google Scholar 

6. Fu, C., Zhang, X., Ji, S., Chen, J., Wu, J., Guo, S., Zhou, J., Liu, A.X. Wang, T.: Label inference attacks against vertical federated learning. In: 31st USENIX Security Symposium (USENIX Security 22), Boston, MA (2022)

7. McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282, PMLR (2017)

8. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. (TIST)10(2), 1–19 (2019)

   Article  Google Scholar 

9. Liu, Y., Kang, Y., Xing, C., Chen, T., Yang, Q.: A secure federated transfer learning framework. IEEE Intell. Syst.35(4), 70–82 (2020)

   Article  Google Scholar 

10. Vepakomma, P., Gupta, O., Swedish, T., Raskar, R.: Split learning for health: Distributed deep learning without sharing raw patient data,arXiv:1812.00564 (2018)

11. Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res.9(11) (2008)

12. Yuan, F., Chen, S., Liang, K., Xu, L.: Research on the coordination mechanism of traditional Chinese medicine medical record data standardization and characteristic protection under big data environment, vol. 1 of1. No.517 Shungong Road, Shizhong District, Jinan, Shandong Province, China: Shandong:Shandong People’s Publishing House, 1 ed., (2021)

13. Chen, C., Huang, T.: Camdar-adv: generating adversarial patches on 3d object. Int. J. Intell. Syst.36(3), 1441–1453 (2021)

    Article MathSciNet  Google Scholar 

14. Jiang, N., Jie, W., Li, J., Liu, X., Jin, D.: Gatrust: A multi-aspect graph attention network model for trust assessment in osns. IEEE Transactions on Knowledge and Data Engineering (2022)

15. Yan, H., Chen, M., Hu, L., Jia, C.: Secure video retrieval using image query on an untrusted cloud. Appl. Soft Comput.97, 106782 (2020)

    Article  Google Scholar 

16. Ai, S., Hong, S., Zheng, X., Wang, Y., Liu, X.: Csrt rumor spreading model based on complex network. Int. J. Intell. Syst.36(5), 1903–1913 (2021)

    Article  Google Scholar 

17. Li, T., Wang, Z., Chen, Y., Li, C., Jia, Y., Yang, Y.: Is semi-selfish mining available without being detected? Int. J. Intell. Syst. (2021).https://doi.org/10.1002/int.22656

    Article  Google Scholar 

18. Li, T., Wang, Z., Chen, Y., Li, C., Jia, Y., Yang, Y.: Is semi-selfish mining available without being detected?. International Journal of Intelligent Systems (2021)

19. Zhang, X., Wang, T.: Elastic and reliable bandwidth reservation based on distributed traffic monitoring and control. IEEE Transactions on Parallel and Distributed Systems (2022)

20. Zhang, X., Wang, Y., Geng, G., Yu, J., Delay-optimized multicast tree packing in software-defined networks. IEEE Transactions on Services Computing (2021)

21. Konečnỳ, J., McMahan, H.B., Ramage, D., Richtárik, P.: Federated optimization: Distributed machine learning for on-device intelligence.arXiv:1610.02527 (2016)

22. Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency.arXiv:1610.05492 (2016)

23. McMahan, H.B., Moore, E., Ramage, D., y Arcas, B. A.: Federated learning of deep networks using model averaging. vol. 2,arXiv:1602.05629 (2016)

24. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191 (2017)

25. Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)

26. Du, W., Atallah, M.J.: Privacy-preserving cooperative statistical analysis. In: Seventeenth Annual Computer Security Applications Conference, pp. 102–110. IEEE (2001)

27. Du, W., Han, Y.S., Chen, S.: Privacy-preserving multivariate statistical analysis: Linear regression and classification. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 222–233. SIAM (2004)

28. Sanil, A.P., Karr, A.F., Lin, X., Reiter, J.P.: Privacy preserving regression modelling via distributed computation. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 677–682 (2004)

29. Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639–644 (2002)

30. Wan, L., Ng, W.K., Han, S., Lee, V.C.: Privacy-preservation for gradient descent methods. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 775–783 (2007)

31. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowledge Data Eng22(10), 1345–1359 (2009)

    Article  Google Scholar 

32. Tianqing, Z., Zhou, W., Ye, D., Cheng, Z., Li, J.: Resource allocation in iot edge computing via concurrent federated reinforcement learning. IEEE Internet of Things Journal9(2), 1414–1426 (2021)

    Article  Google Scholar 

33. Hu, L., Yan, H., Li, L., Pan, Z., Liu, X., Zhang, Z.: Mhat: an efficient model-heterogenous aggregation training scheme for federated learning. Inform. Sci.560, 493–503 (2021)

    Article MathSciNet  Google Scholar 

34. Mo, K., Huang, T., Xiang, X.: Querying little is enough: Model inversion attack via latent information. In: International Conference on Machine Learning for Cyber Security, pp. 583–591. Springer (2020)

35. Ren, H., Huang, T., Yan, H.: Adversarial examples: attacks and defenses in the physical world. International Journal of Machine Learning and Cybernetics12(11), 3325–3336 (2021)

    Article  Google Scholar 

36. Kuang, X., Zhang, M., Li, H., Zhao, G., Cao, H., Wu, Z., Wang, X.: Deepwaf: detecting web attacks based on cnn and lstm models. In: International Symposium on Cyberspace Safety and Security, pp. 121–136. Springer (2019)

37. Yan, H., Hu, L., Xiang, X., Liu, Z., Yuan, X.: Ppcl: Privacy-preserving collaborative learning for mitigating indirect information leakage. Inform. Sci.548, 423–437 (2021)

    Article MathSciNet  Google Scholar 

38. Li, J., Hu, X., Xiong, P., Zhou, W., et al.: The dynamic privacy-preserving mechanisms for online dynamic social networks. IEEE Transactions on Knowledge and Data Engineering (2020)

39. Lu, Z., Liang, H., Zhao, M., Lv, Q., Liang, T., Wang, Y.: Label-only membership inference attacks on machine unlearning without dependence of posteriors. Int. J. Intell. Syst.37(11), 9242–9441 (2022)

    Article  Google Scholar 

40. Melis, L., Song, C., De Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 691–706. IEEE (2019)

41. Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning. In: Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP), pp. 1–15 (2018)

42. Wei, K., Li, J., Ma, C., Ding, M., Wei, S., Wu, F., Chen, G., Ranbaduge, T.: Vertical federated learning: Challenges, methodologies and experiments.arXiv:2202.04309 (2022)

43. Backes, M., Berrang, P., Humbert, M., Manoharan, P.: Membership privacy in microrna-based studies. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 319–330, 2016

44. Chen, D., Yu, N., Zhang, Y., Fritz, M.: Gan-leaks: A taxonomy of membership inference attacks against gans.arXiv:1909.03935 (2019)

45. Pyrgelis, A., Troncoso, C., De Cristofaro, E.: Knock knock, who’s there? membership inference on aggregate location data,arXiv:1708.06145 (2017)

46. Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., Backes, M.: Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models.arXiv:1806.01246 (2018)

47. Rassouli, B., Varasteh, M., Gunduz, D.: Privacy against inference attacks in vertical federated learning.arXiv:2207.11788 (2022)

48. Zhu, H., Wang, R., Jin, Y., Liang, K.: Pivodl: Privacy-preserving vertical federated learning over distributed labels. IEEE Transactions on Artificial Intelligence (2021)

49. Han, X., Wang, L., Wu, J.: Data valuation for vertical federated learning: An information-theoretic approach.arXiv:2112.08364 (2021)

50. Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)

51. Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. Advances in Neural Information Processing Systems,21 (2008)

52. Dwork, C.: Differential privacy: A survey of results. In: International Conference on Theory and Applications of Models of Computation, pp. 1–19. Springer (2008)

53. Song, S., Chaudhuri, K., Sarwate, A.D.: Stochastic gradient descent with differentially private updates. In: 2013 IEEE Global Conference on Signal and Information Processing, pp. 245–248. IEEE (2013)

54. Giacomelli, I., Jha, S. Joye, M., Page, C.D., Yoon, K.: Privacy-preserving ridge regression with only linearly-homomorphic encryption. Cryptology ePrint Archive (2017)

55. Hall, R., Fienberg, S.E., Nardi, Y.: Secure multiple linear regression based on homomorphic encryption. Journal of Official Statistics27(4), 669–691 (2011)

    Google Scholar 

56. Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: 2013 IEEE Symposium on Security and Privacy, pp. 334–348. IEEE (2013)

57. Rivest, R.L., Adleman, L., Dertouzos, M.L., et al.: On data banks and privacy homomorphisms. Foundations of Secure Computation4(11), 169–180 (1978)

    MathSciNet  Google Scholar 

58. Geyer, R.C., Klein, T., Nabi, M.: Differentially private federated learning: A client level perspective.arXiv:1712.07557 (2017)

59. Yuan, J., Yu, S.: Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Transactions on Parallel & Distributed Systems25(01), 212–221 (2014)

    Article  Google Scholar 

60. Zhang, Q., Yang, L.T., Chen, Z.: Privacy preserving deep computation model on cloud for big data feature learning. IEEE Trans. Comput.65(5), 1351–1362 (2015)

    Article MathSciNet MATH  Google Scholar 

61. Araki, T., Furukawa, J., Lindell, Y., Nof, A., Ohara, K.: High-throughput semi-honest secure three-party computation with an honest majority. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 805–817 (2016)

62. Furukawa, J., Lindell, Y., Nof, A., Weinstein, O.: High-throughput secure three-party computation for malicious adversaries and an honest majority. Cryptology ePrint Archive (2016)

63. Mohassel, P., Rosulek, M., Zhang, Y.: Fast and secure three-party computation: The garbled circuit approach. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 591–602 (2015)

64. Zhao, C., Zhao, S., Zhao, M., Chen, Z., Gao, C.-Z., Li, H., Tan, Y.-a.: Secure multi-party computation: theory, practice and applications. Inform. Sci.476, 357–372 (2019)

65. Kilbertus, N., Gascón, A., Kusner, M., Veale, M., Gummadi, K., Weller, A.: Blind justice: Fairness with encrypted sensitive attributes. In: International Conference on Machine Learning, pp. 2630–2639, PMLR (2018)

66. Mohassel, P., Rindal, P.: Aby3: A mixed protocol framework for machine learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 35–52 (2018)

67. Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S.: Federated learning with personalization layers.arXiv:1912.00818 (2019)

68. Jebreel, N.M., Domingo-Ferrer, J., Blanco-Justicia, A., Sanchez, D.: Enhanced security and privacy via fragmented federated learning.arXiv:2207.05978 (2022)

69. Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10713–10722 (2021)

70. Le, P.H., Ranellucci, S., Gordon, S.D.: Two-party private set intersection with an untrusted third party. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 2403–2420 (2019)

Download references