{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:25:39Z","timestamp":1774448739318,"version":"3.50.1"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030812416","type":"print"},{"value":"9783030812423","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-81242-3_2","type":"book-chapter","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T03:04:02Z","timestamp":1626231842000},"page":"22-42","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Comparing Local and Central Differential Privacy Using Membership Inference Attacks"],"prefix":"10.1007","author":[{"given":"Daniel","family":"Bernau","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas","family":"Robl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip W.","family":"Grassal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen","family":"Schneider","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"Kerschbaum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of Conference on Computer and Communications Security (CCS). ACM Press (2016)","DOI":"10.1145\/2976749.2978318"},{"issue":"1","key":"2_CR2","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1257\/aer.20170627","volume":"109","author":"JM Abowd","year":"2019","unstructured":"Abowd, J.M., Schmutte, I.M.: An economic analysis of privacy protection and statistical accuracy as social choices. Am. Econ. Rev. 109(1), 171\u2013202 (2019)","journal-title":"Am. Econ. Rev."},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Backes, M., Berrang, P., Humbert, M., Manoharan, P.: Membership privacy in microRNA-based studies. In: Proceedings of Conference on Computer and Communications Security (CCS). ACM Press (2016)","DOI":"10.1145\/2976749.2978355"},{"key":"2_CR4","unstructured":"Bassily, R., Smith, A., Thakurta, A.: Private empirical risk minimization. In: Proceedings of Symposium on Foundations of Computer Science (FOCS). IEEE Computer Society (2014)"},{"key":"2_CR5","unstructured":"BBC News: Google DeepMind NHS app test broke UK privacy law (2017). https:\/\/www.bbc.com\/news\/technology-40483202"},{"key":"2_CR6","unstructured":"Carlini, N., Liu, C., Kos, J., Erlingsson, \u00da., Song, D.: The secret sharer: measuring unintended neural network memorization and extracting secrets (2018)"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of Conference on Machine Learning (ICML). Omnipress (2006)","DOI":"10.1145\/1143844.1143874"},{"key":"2_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/11787006_1","volume-title":"Automata, Languages and Programming","author":"C Dwork","year":"2006","unstructured":"Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1\u201312. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11787006_1"},{"key":"2_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1007\/11761679_29","volume-title":"Advances in Cryptology - EUROCRYPT 2006","author":"C Dwork","year":"2006","unstructured":"Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: privacy via distributed noise generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486\u2013503. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11761679_29"},{"issue":"3\u20134","key":"2_CR10","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theoret. Comput. Sci. 9(3\u20134), 211\u2013407 (2014)","journal-title":"Found. Trends Theoret. Comput. Sci."},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Erlingsson, U., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., Thakurta, A.: Amplification by shuffling: from local to central differential privacy via anonymity. In: Proceedings of Symposium on Discrete Algorithms (SODA) (2019)","DOI":"10.1137\/1.9781611975482.151"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Erlingsson, U., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of Conference on Computer and Communications Security (CCS). ACM Press (2014)","DOI":"10.1145\/2660267.2660348"},{"issue":"2","key":"2_CR13","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge. Int. J. Comput. Vis. 88(2), 98\u2013136 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"2_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/978-3-319-95729-6_10","volume-title":"Data and Applications Security and Privacy XXXII","author":"L Fan","year":"2018","unstructured":"Fan, L.: Image pixelization with differential privacy. In: Kerschbaum, F., Paraboschi, S. (eds.) DBSec 2018. LNCS, vol. 10980, pp. 148\u2013162. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-95729-6_10"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of Conference on Computer and Communications Security (CCS). ACM Press (2015)","DOI":"10.1145\/2810103.2813677"},{"key":"2_CR16","unstructured":"Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., Ristenpart, T.: Privacy in pharmacogenetics: an end-to-end case study of personalized warfarin dosing. In: Proceedings of USENIX Security Symposium. USENIX Association (2014)"},{"key":"2_CR17","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http:\/\/www.deeplearningbook.org"},{"issue":"4","key":"2_CR18","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1109\/21.370200","volume":"25","author":"Y Grandvalet","year":"1995","unstructured":"Grandvalet, Y., Canu, S.: Comments on \u201cnoise injection into inputs in back propagation learning\u2019\u2019. IEEE Trans. Syst. Man Cybernet. 25(4), 678\u2013681 (1995)","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Hay, M., Machanavajjhala, A., Miklau, G., Chen, Y., Zhang, D.: Principled evaluation of differentially private algorithms using DPBench. In: Proceedings of Conference on Management of Data (SIGMOD). ACM Press (2016)","DOI":"10.1145\/2882903.2882931"},{"issue":"1","key":"2_CR20","first-page":"133","volume":"2019","author":"J Hayes","year":"2019","unstructured":"Hayes, J., Melis, L., Danezis, G., De Cristofaro, E.: LOGAN: membership inference attacks against generative models. Proc. Priv. Enhanc. Technol. (PoPETs) 2019(1), 133\u2013152 (2019)","journal-title":"Proc. Priv. Enhanc. Technol. (PoPETs)"},{"key":"2_CR21","unstructured":"Kashmir Hill: How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did (2012). https:\/\/www.forbes.com\/sites\/kashmirhill\/2012\/02\/16\/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did\/"},{"key":"2_CR22","unstructured":"Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Technical report (2007)"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Iyengar, R., Near, J.P., Song, D., Thakkar, O.D., Thakurta, A., Wang, L.: Towards practical differentially private convex optimization. In: Proceedings of Symposium on Security and Privacy (S&P). IEEE Computer Society (2019)","DOI":"10.1109\/SP.2019.00001"},{"key":"2_CR24","unstructured":"Jayaraman, B., Evans, D.: Evaluating differentially private machine learning in practice. In: Proceedings of the USENIX Security Symposium. USENIX Association (2019)"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Kairouz, P., Oh, S., Viswanath, P.: The composition theorem for differential privacy. IEEE Trans. Inf. Theory 63(6), 4037\u20134049 (2017)","DOI":"10.1109\/TIT.2017.2685505"},{"key":"2_CR26","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1137\/090756090","volume":"40","author":"SP Kasiviswanathan","year":"2008","unstructured":"Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately? SIAM J. Comput. 40, 793\u2013826 (2008)","journal-title":"SIAM J. Comput."},{"key":"2_CR27","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings. ICLR (2015)"},{"issue":"3","key":"2_CR28","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/21.155944","volume":"22","author":"K Matsuoka","year":"1992","unstructured":"Matsuoka, K.: Noise injection into inputs in back-propagation learning. IEEE Trans. Syst. Man Cybernet. 22(3), 36\u2013440 (1992)","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Mironov, I.: R\u00e9nyi differential privacy. In: Proceedings of Computer Security Foundations Symposium (CSF). IEEE Computer Society (2017)","DOI":"10.1109\/CSF.2017.11"},{"key":"2_CR30","unstructured":"MLPerf Website: MLPerf - Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services (2018). https:\/\/mlperf.org\/"},{"key":"2_CR31","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks (2018)"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Machine learning with membership privacy using adversarial regularization. In: Proceedings of Conference on Computer and Communications Security (CCS). ACM Press (2018)","DOI":"10.1145\/3243734.3243855"},{"key":"2_CR33","unstructured":"Papernot, N., Song, S., Mironov, I., Raghunathan, A., Talwar, K., Erlingsson, \u00da.: Scalable private learning with pate (2018)"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference. BMVA Press (2015)","DOI":"10.5244\/C.29.41"},{"key":"2_CR35","first-page":"61","volume":"11","author":"MA Rahman","year":"2018","unstructured":"Rahman, M.A., Rahman, T., Lagani\u00e8re, R., Mohammed, N.: Membership inference attack against differentially private deep learning model. Trans. Data Priv. 11, 61\u201379 (2018)","journal-title":"Trans. Data Priv."},{"key":"2_CR36","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1038\/ng.436","volume":"41","author":"S Sankararaman","year":"2009","unstructured":"Sankararaman, S., Obozinski, G., Jordan, M.I., Halperin, E.: Genomic privacy and limits of individual detection in a pool. Nature Genetics 41, 965\u2013967 (2009)","journal-title":"Nature Genetics"},{"key":"2_CR37","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of Conference on Computer and Communications Security (CCS). ACM Press (2015)","DOI":"10.1109\/ALLERTON.2015.7447103"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against ML models. In: Proceedings of Symposium on Security and Privacy (S&P). IEEE Computer Society (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Song, S., Chaudhuri, K., Sarwate, A.D.: Stochastic gradient descent with differentially private updates. In: Proceedings of Conference on Signal and Information Processing. IEEE Computer Society (2013)","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"2_CR40","unstructured":"Wang, T., Blocki, J., Li, N., Jha, S.: Locally differentially private protocols for frequency estimation. In: Proceedings of USENIX Security Symposium. USENIX Association (2017)"},{"issue":"309","key":"2_CR41","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1080\/01621459.1965.10480775","volume":"60","author":"SL Warner","year":"1965","unstructured":"Warner, S.L.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63\u201369 (1965)","journal-title":"J. Am. Stat. Assoc."},{"key":"2_CR42","unstructured":"Wirth, R., Hipp, J.: Crisp-DM: towards a standard process model for data mining. In: Proceedings of Conference on Practical Applications of Knowledge Discovery and Data Mining. Practical Application Company (2000)"},{"key":"2_CR43","unstructured":"Yeom, S., Fredrikson, M., Jha, S.: The unintended consequences of overfitting: training data inference attacks (2017)"},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting (2018)","DOI":"10.1109\/CSF.2018.00027"}],"container-title":["Lecture Notes in Computer Science","Data and Applications Security and Privacy XXXV"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-81242-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,13]],"date-time":"2025-07-13T22:02:42Z","timestamp":1752444162000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-81242-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030812416","9783030812423"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-81242-3_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"14 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DBSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP Annual Conference on Data and Applications Security and Privacy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Calgary, AB","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dbsec2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wpsites.ucalgary.ca\/dbsec2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}