Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Phân tích chỉ định với bảo mật khác biệt
Tóm tắt
Phân tích chỉ định là một cơ chế cung cấp tập hợp các hành động tốt nhất cần thực hiện để ngăn ngừa những kết quả không mong muốn cho một trường hợp nhất định. Tuy nhiên, cơ chế này dễ bị vi phạm quyền riêng tư nếu một kẻ thù với dữ liệu phụ trợ được cho phép truy cập nhiều lần vào nó. Do đó, chúng tôi đề xuất một cơ chế bảo mật khác biệt trong phân tích chỉ định để bảo vệ quyền riêng tư của dữ liệu. Bảo mật khác biệt có thể đạt được với sự hỗ trợ của độ nhạy của các hành động được cung cấp. Nói chung, độ nhạy là sự thay đổi tối đa trong tập hợp các hành động được cung cấp liên quan đến sự thay đổi trong các trường hợp được đưa ra. Tuy nhiên, một hình thức phân tích tổng quát cho độ nhạy của cơ chế phân tích chỉ định là rất khó để rút ra. Vì vậy, chúng tôi thiết lập một tối ưu hóa ràng buộc lồng nhau để giải quyết vấn đề này. Chúng tôi sử dụng dữ liệu tổng hợp trong các thí nghiệm để xác thực hành vi của cơ chế bảo mật khác biệt liên quan đến các cài đặt tham số bảo mật khác nhau. Các thí nghiệm với hai tập dữ liệu thực tế - Hiệu suất Học thuật của Sinh viên và tập dữ liệu Reddit, chứng minh tính hữu ích của phương pháp mà chúng tôi đề xuất trong thiết kế giáo dục và chính sách xã hội. Chúng tôi cũng đề xuất một chỉ số đánh giá mới gọi là tỷ lệ thành công của đơn thuốc để nghiên cứu thêm về tầm quan trọng của phương pháp mà chúng tôi đề xuất.
Từ khóa
#phân tích chỉ định #bảo mật khác biệt #độ nhạy #tối ưu hóa #chính sách xã hội #giáo dụcTài liệu tham khảo
Achenbach, A., Spinler, S.: Prescriptive analytics in airline operations: arrival time prediction and cost index optimization for short-haul flights. Oper. Res. Perspect. 5, 265–279 (2018)
Aggarwal, C.C., Chen, C., Han, J.: The inverse classification problem. J. Comput. Sci. Technol. 25(3), 458–468 (2010)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. ACM Sigmod Rec. 29, 439–450 (2000)
Anderson, R.N.: ‘Petroleum analytics learning machine’ for optimizing the Internet of Things of today’s digital oil field-to-refinery petroleum system. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4542–4545. IEEE (2017)
Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: Privacy, accuracy, and consistency too: a holistic solution to contingency table release. In: Proceedings of the Twenty-Sixth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 273–282 (2007)
Basu, A.: Five pillars of prescriptive analytics success. Anal. Mag. 8–12 (2013)
Basu, K., Ghosh, S.: Analysis of Thompson sampling for Gaussian process optimization in the bandit setting. arXiv preprint arXiv:1705.06808 (2017)
Baur, A., Klein, R., Steinhardt, C.: Model-based decision support for optimal brochure pricing: applying advanced analytics in the tour operating industry. OR Spectr. 36(3), 557–584 (2014)
Bertsimas, D., Kallus, N.: From predictive to prescriptive analytics. arXiv:1402.5481 (2014)
Brochu, E., Cora, V.M., De Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599 (2010)
Chaudhuri, K., Mishra, N.: When random sampling preserves privacy. In: Annual International Cryptology Conference, pp. 198–213. Springer (2006)
Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. In: Proceedings of Advances in Neural Information Processing Systems, pp. 289–296 (2009)
Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12(3), 1069–1109 (2011)
Chen, H., Fu, C., Zhao, J., Koushanfar, F.: DeepInspect: a Black-box Trojan detection and mitigation framework for deep neural networks. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4658–4664 (2019)
Chi, C.L., Street, W.N., Robinson, J.G., Crawford, M.A.: Individualized patient-centered lifestyle recommendations: an expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options. J. Biomed. Inform. 45(6), 1164–1174 (2012)
Chin, F.Y., Ozsoyoglu, G.: Auditing and inference control in statistical databases. IEEE Trans. Softw. Eng. (6), 574–582 (1982)
Davenport, T.H., et al.: Competing on analytics. Harv. Bus. Rev. 84(1), 98 (2006)
den Hertog, D., Postek, K.: Bridging the Gap Between Predictive and Prescriptive Analytics—New Optimization Methodology Needed. Tilburg University, Tilburg (2016)
Dinur, I., Nissim, K.: Revealing information while preserving privacy. In: Proceedings of the Twenty-Second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 202–210 (2003)
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Theory of Cryptography Conference, vol. 3876, pp. 265–284 (2006)
Dwork, C., Nissim, K.: Privacy-preserving datamining on vertically partitioned databases. In: Annual International Cryptology Conference, pp. 528–544 (2004)
Friedman, A., Schuster, A.: Data mining with differential privacy. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 493–502 (2010)
Ganta, S.R., Kasiviswanathan, S.P., Smith, A.: Composition attacks and auxiliary information in data privacy. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 265–273 (2008)
Gelbart, M.A., Snoek, J., Adams, R.P.: Bayesian optimization with unknown constraints. arXiv:1403.5607 (2014)
Goyal, A., Aprilia, E., Janssen, G., Kim, Y., Kumar, T., Mueller, R., Phan, D., Raman, A., Schuddebeurs, J., Xiong, J., et al.: Asset health management using predictive and prescriptive analytics for the electric power grid. IBM J. Res. Dev. 60(1), 1–4 (2016)
Gröger, C., Schwarz, H., Mitschang, B.: Prescriptive analytics for recommendation-based business process optimization. In: Proceedings of International Conference on Business Information Systems, pp. 25–37 (2014)
Gupta, A., Ligett, K., McSherry, F., Roth, A., Talwar, K.: Differentially private approximation algorithms. In: Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, vol. 1, p. 2 (2010)
Ha, H., Rana, S., Gupta, S., Nguyen, T., Tran-The, H., Venkatesh, S.: Bayesian optimization with unknown search space. In: Proceedings of Advances in Neural Information Processing Systems (2019)
Hagerty, J.: Planning Guide for Data and Analytics, p. 13. Gartner Inc, Stamford (2017)
Harikumar, H., Le, V., Rana, S., Bhattacharya, S., Gupta, S., Venkatesh, S.: Scalable backdoor detection in neural networks. arXiv preprint arXiv:2006.05646 (2020)
Harikumar, H., Nguyen, T., Gupta, S., Rana, S., Kaimal, R., Venkatesh, S.: Understanding behavioral differences between short and long-term drinking abstainers from social media. In: Proceedings of International Conference on Advanced Data Mining and Applications, pp. 520–533 (2016)
Harikumar, H., Nguyen, T., Rana, S., Gupta, S., Kaimal, R., Venkatesh, S.: Extracting key challenges in achieving sobriety through shared subspace learning. In: Proceedings of International Conference on Advanced Data Mining and Applications, pp. 420–433 (2016)
Harikumar, H., Rana, S., Gupta, S., Nguyen, T., Kaimal, R., Venkatesh, S.: Differentially private prescriptive analytics. In: IEEE International Conference on Data Mining, pp. 995–1000 (2018)
Harikumar, H., Rana, S., Gupta, S., Nguyen, T., Kaimal, R., Venkatesh, S.: Prescriptive analytics through constrained Bayesian optimization. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 335–347 (2018)
Hong, S., Shin, S., Kim, Y.M., Seon, C.N., Ho Um, J., Song, S.K.: Design of marketing scenario planning based on business big data analysis. In: International Conference on HCI in Business, pp. 585–592. Springer (2015)
Ilyas, A., Engstrom, L., Athalye, A., Lin, J.: Black-box adversarial attacks with limited queries and information. arXiv preprint arXiv:1804.08598 (2018)
Kusner, M., Gardner, J., Garnett, R., Weinberger, K.: Differentially private Bayesian optimization. In: International Conference on Machine Learning, pp. 918–927 (2015)
Lash, M.T., Lin, Q., Street, N., Robinson, J.G., Ohlmann, J.: Generalized inverse classification. In: Proceedings of the SIAM International Conference on Data Mining, pp. 162–170 (2017)
Lash, M.T., Lin, Q., Street, W.N., Robinson, J.G.: A budget-constrained inverse classification framework for smooth classifiers. In: IEEE International Conference on Data Mining Workshops, pp. 1184–1193 (2017)
Li, X., Zhao, H., Yu, D., Wang, L.E., Liu, P. : Multidimensional correlation hierarchical differential privacy for medical data with multiple privacy requirements. In: International Conference on Healthcare Science and Engineering, pp. 153–173 (2018)
Li, Y., Li, L., Wang, L., Zhang, T., Gong, B.: NATTACK: learning the distributions of adversarial examples for an improved Black-box attack on deep neural networks. arXiv preprint arXiv:1905.00441 (2019)
Liu, F.: Generalized Gaussian mechanism for differential privacy. IEEE Trans. Knowl. Data Eng. 31(4), 747–756 (2019)
McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: 48th Annual IEEE Symposium on Foundations of Computer Science, pp. 94–103 (2007)
Mockus, J.: On Bayesian methods for seeking the extremum and their application. In: Proceedings of the Optimization Techniques IFIP Technical Conference, pp. 400–404 (1975)
Mockus, J.: Application of Bayesian approach to numerical methods of global and stochastic optimization. J. Glob. Optim. 347–365 (1994)
Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy, pp. 111–125 (2008)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pennebaker, J.W., Booth, R.J., Boyd, R.L., Francis, M.E.: Linguistic Inquiry and Word Count: LIWC 2015 [Computer Software]. Pennebaker Conglomerates, Inc., Austin (2015)
Rana, S., Gupta, S.K., Venkatesh, S.: Differentially private random forest with high utility. In: IEEE International Conference on Data Mining, pp. 955–960 (2015)
Ren, X., Yu, C.M., Yu, W., Yang, S., Yang, X., McCann, J.A., Philip, S.Y.: LoPub: high-dimensional crowdsourced data publication with local differential privacy. IEEE Trans. Inf. Forensics Secur. 13(9), 2151–2166 (2018)
Rubinstein, B.I., Bartlett, P.L., Huang, L., Taft, N.: Learning in a large function space: privacy-preserving mechanisms for SVM learning. arXiv:0911.5708 (2009)
Russo, D., Van Roy, B., Kazerouni, A., Osband, I., Wen, Z.: A tutorial on Thompson sampling. arXiv preprint arXiv:1707.02038 (2017)
Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., De Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148–175 (2015)
Liu, L., Özsu, M.: Encyclopedia of Database Systems, vol. 6, Springer New York, NY, USA (2009)
Smith, M., Álvarez, M., Zwiessele, M., Lawrence, N.D.: Differentially private regression with Gaussian processes. In: International Conference on Artificial Intelligence and Statistics, pp. 1195–1203 (2018)
Smith, M.T., Zwiessele, M., Lawrence, N.D.: Differentially private Gaussian processes. arXiv:1606.00720 (2016)
Song, S., Chaudhuri, K., Sarwate, A.D.: Stochastic gradient descent with differentially private updates. In: Global Conference on Signal and Information Processing, pp. 245–248 (2013)
Song, S.K., Jeong, D.H., Kim, J., Hwang, M., Gim, J., Jung, H.: Research advising system based on prescriptive analytics. In: Future Information Technology. Springer, pp. 569–574 (2014)
Srinivas, N., Krause, A., Kakade, S., Seeger, M.: Gaussian process optimization in the bandit setting: no regret and experimental design. In: In International Conference on Machine Learning, pp. 1015–1022 (2010)
Sweeney, L.: k-Anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 557–570 (2002)
Thompson, W.R.: On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25(3/4), 285–294 (1933)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 58(1), 267–288 (1996)
Traub, J.F., Yemini, Y., Woźniakowski, H.: The statistical security of a statistical database. ACM Trans. Database Syst. 9(4), 672–679 (1984)
Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H., Zhao, B.Y.: Neural cleanse: identifying and mitigating backdoor attacks in neural networks. In: IEEE Symposium on Security and Privacy, pp. 707–723 (2019)
Wu, P.J., Yang, C.K.: The green fleet optimization model for a low-carbon economy: a prescriptive analytics. In: 2017 International Conference on Applied System Innovation (ICASI), pp. 107–110. IEEE (2017)
Yang, C., Street, N.W., Robinson, J.G.: 10-year CVD risk prediction and minimization via inverse classification. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 603–610 (2012)