Open Access
Issue
Security and Safety
Volume 1, 2022
Article Number 2022008
Number of page(s) 18
Section Social Governance
DOI https://doi.org/10.1051/sands/2022008
Published online 25 July 2022
  1. Han J, Pei J and Kamber M. Data Mining: Concepts and Techniques. The Netherlands: Elsevier, 2011. [Google Scholar]
  2. Jia JS, Lu X and Yuan Y et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 2020, 582: 389–94. [Google Scholar]
  3. Park Y and Ho JC. Tackling overfitting in boosting for noisy healthcare data. IEEE Trans Knowl Data Eng 2021; 33: 2995–3006. [Google Scholar]
  4. Wang W, Lesner C and Ran A et al. Using small business banking data for explainable credit risk scoring. Proc AAAI Conf Artif Intell 2020; 34: 13396–401. [Google Scholar]
  5. Liu Y, Ao X and Zhong Q et al. Alike and unlike: Resolving class imbalance problem in financial credit risk assessment. In: Proc. 29th ACM Int. Conf. Inf. Knowl. Manag. Virtual Event, Ireland, Oct. 19-23, 2020, 2125–8. [Google Scholar]
  6. De Montjoye YA, Radaelli L and Singh VK et al. Unique in the shopping mall: On the reidentifiability of credit card metadata. Science 2015; 347: 536–9. [Google Scholar]
  7. Zhang L, Shen J and Zhang J et al. Multimodal marketing intent analysis for effective targeted advertising. IEEE Trans Multim 2022; 24: 1830–43. [Google Scholar]
  8. Deng A and Hooi B. Graph neural network-based anomaly detection in multivariate time series. Proc AAAI Conf Artif Intell 2021; 35: 4027–35. [Google Scholar]
  9. Hu W, Gao J and Li B et al. Anomaly detection using local kernel density estimation and context-based regression. IEEE Trans Knowl Data Eng 2018; 32: 218–33. [Google Scholar]
  10. Wu FJ and Luo T. Crowdprivacy: Publish more useful data with less privacy exposure in crowdsourced location-based services. ACM Trans Priv Secur 2020; 23: 6:1–25. [Google Scholar]
  11. Holt JD and Chung SM. Efficient mining of association rules in text databases. In: Proc. 1999 ACM CIKM Int. Conf. Inf. Knowl. Manag., Kansas City, Missouri, USA, Nov. 2-6, 1999, 234–42. [Google Scholar]
  12. Sankar L, Rajagopalan SR and Poor HV. Utility-privacy tradeoffs in databases: An information-theoretic approach. IEEE Trans Inf Forensics Secur 2013, 8: 838–52. [Google Scholar]
  13. Narayanan A and Shmatikov V. Robust de-anonymization of large sparse datasets. In: 2008 IEEE Symp. Secur. Priv. (SP), Oakland, CA, USA, May 18-22, 2008, 111–25. [CrossRef] [Google Scholar]
  14. Li S, Ji X and You W. A personalized differential privacy protection method for repeated queries. In: 2019 IEEE 4th Int. Conf. Big Data Anal. (ICBDA), Suzhou, China, Mar. 15-18, 2019, 274–280. [CrossRef] [Google Scholar]
  15. Dwork C and Roth A. The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 2014; 9: 211–407. [Google Scholar]
  16. Machanavajjhala A, Kifer D and Gehrke J et al. L-diversity: Privacy beyond k-anonymity. ACM Trans Knowl Discov Data 2007; 1: 3. [Google Scholar]
  17. Li N, Li T and Venkatasubramanian S. t-closeness: Privacy beyond k-anonymity and l-diversity. In: Proc. 23rd Int. Conf. Data Eng., The Marmara Hotel, Istanbul, Turkey, Apr. 15-20, 2007, 106–15. [Google Scholar]
  18. Yang Q, Liu Y and Chen T et al. Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol 2019; 10: 12:1–19. [Google Scholar]
  19. Mohassel P and Zhang Y. Secureml: A system for scalable privacy-preserving machine learning. In: 2017 IEEE Symp. Secur. Priv. (SP), San Jose, CA, USA, May 22-26, 2017, 19–38. [CrossRef] [Google Scholar]
  20. Chen H, Dai W and Kim M et al. Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference. In Proc. 2019 ACM SIGSAC Conf. Comput. Commun. Secur., CCS 2019, London, UK, Nov. 11-15, 2019, 395–412. [Google Scholar]
  21. Fredrikson M, Lantz E and Jha S et al. Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing. In: Proc. 23rd USENIX Secur. Symp., San Diego, CA, USA, Aug. 20-22, 2014, 17–32. [Google Scholar]
  22. Krause A and Horvitz E. A utility-theoretic approach to privacy and personalization. In: Proc. Twenty-Third AAAI Conf. Artif. Intell., Chicago, Illinois, USA, July 13-17, 2008, Vol. 8, 1181–8. [Google Scholar]
  23. Gross R, Airoldi E and Malin B et al. Integrating Utility into Face De-identification. Berlin, Heidelberg: Springer, 2006. [Google Scholar]
  24. Yang Q, Wang C and Wang C et al. Fundamental limits of data utility: A case study for data-driven identity authentication. IEEE Trans Comput Soc Syst 2020; 8: 398–409. [Google Scholar]
  25. Datta A, Fredrikson M and Ko G et al. Use privacy in data-driven systems: Theory and experiments with machine learnt programs. In: Proc. 2017 ACM SIGSAC Conf. Comput. Commun. Secur., CCS 2017, Dallas, TX, USA, Oct. 30-Nov. 03, 2017, 1193–210. [Google Scholar]
  26. Tseng BW and Wu PY. Compressive privacy generative adversarial network. IEEE Trans Inf Forensics Secur 2020; 15: 2499–513. [Google Scholar]
  27. Kim H, Park J and Min K et al. Anomaly monitoring framework in lane detection with a generative adversarial network. IEEE Trans Intell Transp Syst 2020; 22: 1603–15. [Google Scholar]
  28. Ruffino C, Hérault R and Laloy E et al. Pixel-wise conditioned generative adversarial networks for image synthesis and completion. Neurocomputing 2020; 416: 218–30. [Google Scholar]
  29. Zhang K, Zhong G and Dong J et al. Stock market prediction based on generative adversarial network. Proc Comput Sci 2019; 147: 400–6. [Google Scholar]
  30. Xie L, Lin K and Wang S et al. Differentially private generative adversarial network, arXiv preprint arXiv:1802.06739, 2018. [Google Scholar]
  31. Jordon J, Yoon J and van der Schaar M. PATE-GAN: Generating synthetic data with differential privacy guarantees. In: 7th Int. Conf. Learn. Represent., ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. [Google Scholar]
  32. Warner SL. Randomized response: A survey technique for eliminating evasive answer bias. J Am Stat Assoc 1965; 60: 63–9. [Google Scholar]
  33. Aggarwal CC and Philip SY. A Condensation Approach to Privacy Preserving Data Mining. Berlin, Heidelberg: Springer, 2004. [Google Scholar]
  34. Dwork C. Differential privacy. In: Autom. Lang. Program. 33rd Int. Colloq. ICALP 2006, Venice, Italy, Jul. 10-14, 2006, Proc. Part II, 2006, 1–12. [Google Scholar]
  35. Sweeney L. k-anonymity: A model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 2002; 10: 557–70. [Google Scholar]
  36. Zhao C, Zhao S and Zhao M et al. Secure multi-party computation: Theory, practice and applications. Inf Sci 2019; 476: 357–72. [Google Scholar]
  37. Elgamal T. A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans Inf Theor 1985; 31: 469–72. [Google Scholar]
  38. Ullah S, Li XY and Hussain MT et al. Kernel homomorphic encryption protocol. J Inf Secur Appl 2049; 48: 102366. [Google Scholar]
  39. Kairouz P, Oh S and Viswanath P. Extremal mechanisms for local differential privacy. J Mach Learn Res 2016; 17: 492–542. [Google Scholar]
  40. Goodfellow I, Pouget-Abadie J and Mirza M et al. Generative adversarial nets. In: Adv. Neural Inf. Process. Syst. 27: Annu. Conf. Neural Inf. Process. Syst. 2014, Montreal, Quebec, Canada, Dec. 8-13, 2014, 2672–2680. [Google Scholar]
  41. Mirza M and Osindero S. Conditional generative adversarial nets, arXiv preprint arXiv:1411.1784, 2014. [Google Scholar]
  42. Torra V. Data Privacy: Foundations, New Developments and the Big Data Challenge. Heidelberg: Springer, 2017. [CrossRef] [Google Scholar]
  43. Xu L, Jiang C and Wang J et al. Information security in big data: Privacy and data mining. IEEE Access 2014; 2: 1149–76. [Google Scholar]
  44. Estévez PA, Tesmer M and Perez CA et al. Normalized mutual information feature selection. IEEE Trans Neural Netw 2009; 20: 189–201. [Google Scholar]
  45. Jaynes ET. Information theory and statistical mechanics. Phys Rev 1957; 106: 620. [CrossRef] [Google Scholar]
  46. Benesty J, Chen J and Huang Y et al. Pearson correlation coefficient. Berlin, Heidelberg: Springer, 2009. [Google Scholar]
  47. Nargesian F, Samulowitz H and Khurana U et al. Learning feature engineering for classification, In: Proc. Twenty-Sixth Int. Jt. Conf. Artif. Intell., IJCAI 2017, Melbourne, Australia, Aug. 19-25, 2017, 2529–35. [Google Scholar]
  48. Datta A, Sen S and Zick Y. Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In: 2016 IEEE Symp. Secur. Priv. (SP), San Jose, CA, USA, May 22-26, 2016, 598–617. [CrossRef] [Google Scholar]
  49. Hild II KE, Erdogmus D and Torkkola K et al. Feature extraction using information-theoretic learning. IEEE Trans Pattern Anal Mach Intell 2006; 28: 1385–92. [Google Scholar]
  50. Kingma DP and Welling M. Auto-encoding variational bayes. In: Bengio Y and LeCun Y, editors, 2nd Int. Conf. Learn. Represent., ICLR 2014, Ban, AB, Canada, Apr. 14-16, 2014, Conf. Track Proc., 2014. [Google Scholar]
  51. Menéndez ML, Pardo JA and Pardo L et al. The jensen-shannon divergence. J Frankl Inst 1997; 334: 307–18. [Google Scholar]
  52. Chen T and Guestrin C. XGBoost: A scalable tree boosting system. In: Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, San Francisco, CA, USA, Aug. 13-17, 2016, 785–94. [CrossRef] [Google Scholar]
  53. Friedman JH. Greedy function approximation: A gradient boosting machine. Annal Stat 2001; 29: 1189–232. [Google Scholar]
  54. Li Q, Wen Z and He B. Practical federated gradient boosting decision trees. Proc AAAI Conf Artif Intell 2020; 34: 4642–9. [Google Scholar]
  55. Breiman L. Random forests. Mach Learn 2001; 45: 5–32. [Google Scholar]
  56. Anthony M and Bartlett PL. Neural Network Learning: Theoretical Foundations. Cambridge: Cambridge University Press, 2009. [Google Scholar]
  57. Pan X and Xu Y. A safe feature elimination rule for L1-regularized logistic regression. IEEE Trans Pattern Anal Mach Intell 2021. [PubMed] [Google Scholar]
  58. Fehr S and Berens S. On the conditional Rényi entropy. IEEE Trans Inf Theor 2014; 60: 6801–10. [Google Scholar]

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