{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:17:46Z","timestamp":1778782666654,"version":"3.51.4"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2015,8]]},"abstract":"<jats:p>Differential privacy (DP) has been widely explored in academia recently but less so in industry possibly due to its strong privacy guarantee. This paper makes the first attempt to implement three basic DP architectures in the deployed telecommunication (telco) big data platform for data mining applications. We find that all DP architectures have less than 5% loss of prediction accuracy when the weak privacy guarantee is adopted (e.g., privacy budget parameter \u03b5 \u2265 3). However, when the strong privacy guarantee is assumed (e.g., privacy budget parameter \u03b5 \u2264 0:1), all DP architectures lead to 15% ~ 30% accuracy loss, which implies that real-word industrial data mining systems cannot work well under such a strong privacy guarantee recommended by previous research works. Among the three basic DP architectures, the Hybridized DM (Data Mining) and DB (Database) architecture performs the best because of its complicated privacy protection design for the specific data mining algorithm. Through extensive experiments on big data, we also observe that the accuracy loss increases by increasing the variety of features, but decreases by increasing the volume of training data. Therefore, to make DP practically usable in large-scale industrial systems, our observations suggest that we may explore three possible research directions in future: (1) Relaxing the privacy guarantee (e.g., increasing privacy budget \u03b5) and studying its effectiveness on specific industrial applications; (2) Designing specific privacy scheme for specific data mining algorithms; and (3) Using large volume of data but with low variety for training the classification models.<\/jats:p>","DOI":"10.14778\/2824032.2824067","type":"journal-article","created":{"date-parts":[[2015,9,16]],"date-time":"2015-09-16T12:18:17Z","timestamp":1442405897000},"page":"1692-1703","source":"Crossref","is-referenced-by-count":48,"title":["Differential privacy in telco big data platform"],"prefix":"10.14778","volume":"8","author":[{"given":"Xueyang","family":"Hu","sequence":"first","affiliation":[{"name":"Huawei Noah's Ark Lab, Hong Kong and Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguo","family":"Yao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Deng","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibing","family":"Guan","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Zeng","sequence":"additional","affiliation":[{"name":"Soochow University and Huawei Noah's Ark Lab, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2015,8]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"19","article-title":"binti Mohd Shukor, N. 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H.","year":"1980","unstructured":"L. H. Cox . Suppression methodology and statistical disclosure control . Journal of the American Statistical Association , 75 ( 370 ): 377 -- 385 , 1980 . L. H. Cox. Suppression methodology and statistical disclosure control. Journal of the American Statistical Association, 75(370):377--385, 1980.","journal-title":"Journal of the American Statistical Association"},{"issue":"3","key":"e_1_2_1_4_1","first-page":"329","article-title":"nding a needle in a haystack or identifying anonymous census records","volume":"2","author":"Dalenius T.","year":"1986","unstructured":"T. Dalenius . nding a needle in a haystack or identifying anonymous census records . Journal of Official Statistics , 2 ( 3 ): 329 -- 336 , 1986 . T. Dalenius. nding a needle in a haystack or identifying anonymous census records. Journal of Official Statistics, 2(3):329--336, 1986.","journal-title":"Journal of Official Statistics"},{"key":"e_1_2_1_5_1","first-page":"990","volume-title":"Third International Conference on Availability, Reliability and Security (ARES 08)","author":"Domingo-Ferrer J.","year":"2008","unstructured":"J. Domingo-Ferrer and V. Torra . A Critique of k-Anonymity and Some of Its Enhancements . In Third International Conference on Availability, Reliability and Security (ARES 08) , pages 990 -- 993 . IEEE, 2008 . 10.1109\/ARES.2008.97 J. Domingo-Ferrer and V. Torra. A Critique of k-Anonymity and Some of Its Enhancements. In Third International Conference on Availability, Reliability and Security (ARES 08), pages 990--993. 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ACM. 10.1145\/1835804.1835868"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835868"},{"key":"e_1_2_1_9_1","volume-title":"Analysis of the KDD Cup 2009: Fast scoring on a large orange customer database. 7: 1--22","author":"Guyon I.","year":"2009","unstructured":"I. Guyon , V. Lemaire , M. Boull\u00e9 , G. Dror , and D. Vogel . Analysis of the KDD Cup 2009: Fast scoring on a large orange customer database. 7: 1--22 , 2009 . I. Guyon, V. Lemaire, M. Boull\u00e9, G. Dror, and D. Vogel. Analysis of the KDD Cup 2009: Fast scoring on a large orange customer database. 7: 1--22, 2009."},{"key":"e_1_2_1_10_1","volume-title":"Data Mining: Concepts and Techniques","author":"Han J.","year":"2005","unstructured":"J. Han . Data Mining: Concepts and Techniques . Morgan Kaufmann Publishers Inc ., San Francisco, CA, USA, 2005 . J. Han. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732951.2732966"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1145\/2723372.2742794","volume-title":"Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15","author":"Huang Y.","year":"2015","unstructured":"Y. Huang , F. Zhu , M. Yuan , K. Deng , Y. Li , B. Ni , W. Dai , Q. Yang , and J. Zeng . Telco churn prediction with big data . In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15 , pages 607 -- 618 , Melbourne, VC, AUS , 2015 . ACM. 10.1145\/2723372.2742794 Y. Huang, F. Zhu, M. Yuan, K. Deng, Y. Li, B. Ni, W. Dai, Q. Yang, and J. Zeng. Telco churn prediction with big data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15, pages 607--618, Melbourne, VC, AUS, 2015. 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In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, SIGMOD '05, pages 49--60, New York, NY, USA, 2005. ACM. 10.1145\/1066157.1066164"},{"issue":"2","key":"e_1_2_1_16_1","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1509\/jmkr.43.2.276","article-title":"Bagging and boosting classification trees to predict churn","volume":"43","author":"Lemmens A.","year":"2006","unstructured":"A. Lemmens and C. Croux . Bagging and boosting classification trees to predict churn . Journal of Marketing Research , 43 ( 2 ): 276 -- 286 , 2006 . A. Lemmens and C. Croux. Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43(2):276--286, 2006.","journal-title":"Journal of Marketing Research"},{"key":"e_1_2_1_17_1","first-page":"106","volume-title":"ICDE","author":"Li N.","year":"2007","unstructured":"N. Li , T. Li , and S. Venkatasubramanian . t-closeness: Privacy beyond k-anonymity and l-diversity . In ICDE , pages 106 -- 115 . IEEE, 2007 . N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and l-diversity. In ICDE, pages 106--115. IEEE, 2007."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350251"},{"issue":"8","key":"e_1_2_1_19_1","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1057\/jors.2008.161","article-title":"Domain knowledge integration in data mining using decision tables: Case studies in churn prediction","volume":"60","author":"Lima E.","year":"2009","unstructured":"E. Lima , C. Mues , and B. Baesens . Domain knowledge integration in data mining using decision tables: Case studies in churn prediction . Journal of the Operational Research Society , 60 ( 8 ): 1096 -- 1106 , 2009 . E. Lima, C. Mues, and B. Baesens. Domain knowledge integration in data mining using decision tables: Case studies in churn prediction. 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In SIGMOD'08, pages 93--106, 2008. 10.1145\/1376616.1376629"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2006.1"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217302"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.14778\/1988776.1988780"},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/1559845.1559850","volume-title":"Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD '09","author":"McSherry F. D.","year":"2009","unstructured":"F. D. McSherry . Privacy integrated queries: An extensible platform for privacy-preserving data analysis . In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD '09 , pages 19 -- 30 , New York, NY, USA , 2009 . ACM. 10.1145\/1559845.1559850 F. D. McSherry. Privacy integrated queries: An extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD '09, pages 19--30, New York, NY, USA, 2009. ACM. 10.1145\/1559845.1559850"},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1145\/2020408.2020487","volume-title":"Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11","author":"Mohammed N.","year":"2011","unstructured":"N. Mohammed , R. Chen , B. C. Fung , and P. S. Yu . Differentially private data release for data mining . In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11 , pages 493 -- 501 , New York, NY, USA , 2011 . ACM. 10.1145\/2020408.2020487 N. Mohammed, R. Chen, B. C. Fung, and P. S. Yu. Differentially private data release for data mining. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11, pages 493--501, New York, NY, USA, 2011. ACM. 10.1145\/2020408.2020487"},{"key":"e_1_2_1_26_1","volume-title":"Predicting near-future churners and win-backs in the telecommunications industry. arXiv preprint arXiv:1210.6891","author":"Phua C.","year":"2012","unstructured":"C. Phua , H. Cao , J. B. Gomes , and M. N. Nguyen . Predicting near-future churners and win-backs in the telecommunications industry. arXiv preprint arXiv:1210.6891 , 2012 . C. Phua, H. Cao, J. B. Gomes, and M. N. Nguyen. Predicting near-future churners and win-backs in the telecommunications industry. arXiv preprint arXiv:1210.6891, 2012."},{"key":"e_1_2_1_27_1","volume-title":"2013 IEEE 29th International Conference on Data Engineering (ICDE), 0: 757--768, 2013","author":"Qardaji W.","year":"2013","unstructured":"W. Qardaji , W. Yang , and N. Li . Differentially private grids for geospatial data . 2013 IEEE 29th International Conference on Data Engineering (ICDE), 0: 757--768, 2013 . 10.1109\/ICDE. 2013 .6544872 W. Qardaji, W. Yang, and N. Li. Differentially private grids for geospatial data. 2013 IEEE 29th International Conference on Data Engineering (ICDE), 0: 757--768, 2013. 10.1109\/ICDE.2013.6544872"},{"key":"e_1_2_1_28_1","first-page":"337","volume-title":"PVLDB","volume":"6","author":"Rendle S.","year":"2013","unstructured":"S. Rendle . Scaling factorization machines to relational data . In PVLDB , volume 6 , pages 337 -- 348 , 2013 . 10.14778\/2535573.2488340 S. Rendle. Scaling factorization machines to relational data. In PVLDB, volume 6, pages 337--348, 2013. 10.14778\/2535573.2488340"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1142\/S021848850200165X"},{"key":"e_1_2_1_30_1","volume-title":"Introduction to Data Mining","author":"Tan P.-N.","year":"2005","unstructured":"P.-N. Tan , M. Steinbach , and V. Kumar . Introduction to Data Mining , ( First Edition). Addison-Wesley Longman Publishing Co., Inc. , Boston, MA, USA , 2005 . P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining, (First Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2005."},{"key":"e_1_2_1_31_1","first-page":"1054","volume-title":"21st ACM Conference on Computer and Communications Security","author":"Ulfar E.","year":"2014","unstructured":"E. Ulfar , P. Vasyl , and K. Aleksandra . Rappor: Randomized aggregatable privacy-preserving ordinal response . In 21st ACM Conference on Computer and Communications Security , pages 1054 -- 1067 , 2014 . 10.1145\/2660267.2660348 E. Ulfar, P. Vasyl, and K. Aleksandra. Rappor: Randomized aggregatable privacy-preserving ordinal response. In 21st ACM Conference on Computer and Communications Security, pages 1054--1067, 2014. 10.1145\/2660267.2660348"},{"key":"e_1_2_1_32_1","volume-title":"Turning telecommunications call details to churn prediction: a data mining approach. Expert systems with applications, 23(2):103--112","author":"Wei C.-P.","year":"2002","unstructured":"C.-P. Wei and I. Chiu . Turning telecommunications call details to churn prediction: a data mining approach. Expert systems with applications, 23(2):103--112 , 2002 . C.-P. Wei and I. Chiu. Turning telecommunications call details to churn prediction: a data mining approach. Expert systems with applications, 23(2):103--112, 2002."},{"key":"e_1_2_1_33_1","first-page":"543","volume-title":"Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB '07","author":"Wong R. C.-W.","year":"2007","unstructured":"R. C.-W. Wong , A. W.-C. Fu , K. Wang , and J. Pei . Minimality attack in privacy preserving data publishing . In Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB '07 , pages 543 -- 554 . VLDB Endowment , 2007 . R. C.-W. Wong, A. W.-C. Fu, K. Wang, and J. Pei. Minimality attack in privacy preserving data publishing. In Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB '07, pages 543--554. VLDB Endowment, 2007."},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1145\/2623330.2623642","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14","author":"Xiao Q.","year":"2014","unstructured":"Q. Xiao , R. Chen , and K.-L. Tan . Differentially private network data release via structural inference . In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14 , pages 911 -- 920 , New York, NY, USA , 2014 . ACM. 10.1145\/2623330.2623642 Q. Xiao, R. Chen, and K.-L. Tan. Differentially private network data release via structural inference. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, pages 911--920, New York, NY, USA, 2014. 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Ho , C.-F. Chang , Y.-H. Wei , Feature engineering and classifier ensemble for kdd cup 2010 . In JMLR W & CP , pages 1 -- 16 , 2010 . H.-F. Yu, H.-Y. Lo, H.-P. Hsieh, J.-K. Lou, T. G. McKenzie, J.-W. Chou, P.-H. Chung, C.-H. Ho, C.-F. Chang, Y.-H. Wei, et al. Feature engineering and classifier ensemble for kdd cup 2010. In JMLR W & CP, pages 1--16, 2010."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.14778\/1921071.1921080"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.14778\/2428536.2428539"},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1145\/2588555.2588573","volume-title":"Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD '14","author":"Zhang J.","year":"2014","unstructured":"J. Zhang , G. Cormode , C. M. Procopiuc , D. Srivastava , and X. Xiao . Privbayes: Private data release via bayesian networks . 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