{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:27:24Z","timestamp":1722385644027},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation programme","award":["835294"]},{"name":"Cybersecurity Center research grant at Prince Mohmed University","award":["PCC-Grant-202229"]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s10618-024-01031-0","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T09:02:07Z","timestamp":1716800527000},"page":"2252-2275","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["On the impact of multi-dimensional local differential privacy on fairness"],"prefix":"10.1007","volume":"38","author":[{"given":"Karima","family":"Makhlouf","sequence":"first","affiliation":[]},{"given":"H\u00e9ber H.","family":"Arcolezi","sequence":"additional","affiliation":[]},{"given":"Sami","family":"Zhioua","sequence":"additional","affiliation":[]},{"given":"Ghassen Ben","family":"Brahim","sequence":"additional","affiliation":[]},{"given":"Catuscia","family":"Palamidessi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"1031_CR1","doi-asserted-by":"crossref","unstructured":"Alves G, Bernier F, Couceiro M, Makhlouf K, Palamidessi C, Zhioua S (2022) Survey on fairness notions and related tensions. arXiv preprint arXiv:2209.13012","DOI":"10.1016\/j.ejdp.2023.100033"},{"key":"1031_CR2","unstructured":"Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias. propublica. See https:\/\/www. propublica. org\/article\/machine-bias-risk-assessments-in-criminal-sentencing"},{"key":"1031_CR3","doi-asserted-by":"crossref","unstructured":"Arcolezi HH, Couchot JF, Al Bouna B, Xiao X (2021) Random sampling plus fake data: multidimensional frequency estimates with local differential privacy. In: Proceedings of the 30th ACM international conference on information & knowledge management, CIKM \u201921, New York, NY, USA . Association for Computing Machinery, pp 47\u201357","DOI":"10.1145\/3459637.3482467"},{"key":"1031_CR4","unstructured":"Arcolezi HH, Couchot JF, Al Bouna B, Xiao X (2022) Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates. Digit Commun Netw"},{"key":"1031_CR5","doi-asserted-by":"crossref","unstructured":"Arcolezi HH, Couchot JF, Gambs S, Palamidessi C, Zolfaghari M (2022) Multi-Freq-LDPy: multiple frequency estimation under local differential privacy in python. In: Atluri V, Di\u00a0Pietro R, Jensen CD, Meng W (eds) Computer Security\u2014ESORICS 2022. Springer Nature Switzerland, Cham, pp 770\u2013775","DOI":"10.1007\/978-3-031-17143-7_40"},{"key":"1031_CR6","doi-asserted-by":"crossref","unstructured":"Arcolezi HH, Makhlouf K, Palamidessi C (2023) (Local) differential privacy has NO disparate impact on\u00a0fairness. In: Data and applications security and privacy XXXVII. Springer Nature Switzerland, pp 3\u201321","DOI":"10.1007\/978-3-031-37586-6_1"},{"key":"1031_CR7","unstructured":"Bagdasaryan E, Poursaeed O, Shmatikov V (2019) Differential privacy has disparate impact on model accuracy. Adv Neural Inf Process Syst 32"},{"key":"1031_CR8","unstructured":"Barocas S, Hardt M, Narayanan A (2019) Fairness and machine learning. http:\/\/www.fairmlbook.org"},{"issue":"1","key":"1031_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0049124118782533","volume":"50","author":"R Berk","year":"2021","unstructured":"Berk R, Heidari H, Jabbari S, Kearns M, Roth A (2021) Fairness in criminal justice risk assessments: the state of the art. Sociol. Methods Res. 50(1):3\u201344","journal-title":"Sociol. Methods Res."},{"key":"1031_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"1031_CR11","doi-asserted-by":"crossref","unstructured":"Chang H, Shokri R (2021) On the privacy risks of algorithmic fairness. In 2021 IEEE European symposium on security and privacy (EuroS &P). IEEE, pp 292\u2013303","DOI":"10.1109\/EuroSP51992.2021.00028"},{"key":"1031_CR12","unstructured":"Chen C, Liang Y, Xu X, Xie S, Kundu A, Payani A, Hong Y, Shu K (2022) When fairness meets privacy: Fair classification with semi-private sensitive attributes. In: Workshop on trustworthy and socially responsible machine learning, NeurIPS 2022"},{"issue":"2","key":"1031_CR13","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1089\/big.2016.0047","volume":"5","author":"A Chouldechova","year":"2017","unstructured":"Chouldechova A (2017) Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big data 5(2):153\u2013163","journal-title":"Big data"},{"key":"1031_CR14","doi-asserted-by":"crossref","unstructured":"Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 797\u2013806","DOI":"10.1145\/3097983.3098095"},{"key":"1031_CR15","unstructured":"da Costa Filho JS, Machado JC (2023) FELIP: a local differentially private approach to frequency estimation on multidimensional datasets. In: Proceedings of the 26th international conference on extending database technology, EDBT 2023, Ioannina, Greece, March 28\u201331, 2023, pp 671\u2013683. OpenProceedings.org"},{"key":"1031_CR16","unstructured":"de Oliveira AS, Kaplan C, Mallat K, Chakraborty T (2023) An empirical analysis of fairness notions under differential privacy. arXiv preprint arXiv:2302.02910"},{"key":"1031_CR17","unstructured":"Differential Privacy\u00a0Team Apple (2017) Learning with privacy at scale"},{"key":"1031_CR18","first-page":"6478","volume":"34","author":"F Ding","year":"2021","unstructured":"Ding F, Hardt M, Miller J, Schmidt L (2021) Retiring adult: new datasets for fair machine learning. Adv Neural Inf Process Syst 34:6478\u20136490","journal-title":"Adv Neural Inf Process Syst"},{"issue":"10","key":"1031_CR19","doi-asserted-by":"publisher","first-page":"4933","DOI":"10.1109\/TKDE.2020.3045759","volume":"34","author":"J Domingo-Ferrer","year":"2022","unstructured":"Domingo-Ferrer J, Soria-Comas J (2022) Multi-dimensional randomized response. IEEE Trans Knowl Data Eng 34(10):4933\u20134946","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1031_CR20","unstructured":"Dua D, Graff C (2017) UCI machine learning repository"},{"key":"1031_CR21","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/11681878_14","volume-title":"Theory of cryptography","author":"C Dwork","year":"2006","unstructured":"Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Reingold O (ed) Theory of cryptography. Springer, Berlin, pp 265\u2013284"},{"key":"1031_CR22","doi-asserted-by":"crossref","unstructured":"Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214\u2013226","DOI":"10.1145\/2090236.2090255"},{"key":"1031_CR23","doi-asserted-by":"crossref","unstructured":"Dwork C, Roth A (2014) et\u00a0al. The algorithmic foundations of differential privacy. Found Trends\u00ae Theor Comput Sci 9(3\u20134):211\u2013407","DOI":"10.1561\/0400000042"},{"key":"1031_CR24","doi-asserted-by":"crossref","unstructured":"Erlingsson \u00da, Pihur V, Korolova A (2014) Rappor: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pp 1054\u20131067","DOI":"10.1145\/2660267.2660348"},{"key":"1031_CR25","doi-asserted-by":"crossref","unstructured":"Farrand T, Mireshghallah F, Singh S, Trask A (2020) Neither private nor fair: impact of data imbalance on utility and fairness in differential privacy. In: Proceedings of the 2020 workshop on privacy-preserving machine learning in practice, pp 15\u201319","DOI":"10.1145\/3411501.3419419"},{"key":"1031_CR26","unstructured":"Ficiu B, Lawrence ND, Paleyes A (2023) Automated discovery of trade-off between utility, privacy and fairness in machine learning models. arXiv preprint arXiv:2311.15691"},{"key":"1031_CR27","doi-asserted-by":"crossref","unstructured":"Fioretto F, Tran C, Van Hentenryck P, Zhu K (2022) Differential privacy and fairness in decisions and learning tasks: a survey. arXiv preprint arXiv:2202.08187","DOI":"10.24963\/ijcai.2022\/766"},{"key":"1031_CR28","unstructured":"Ganev G, Oprisanu B, De Cristofaro E (2022) Robin hood and Matthew effects: differential privacy has disparate impact on synthetic data. In: International conference on machine learning. PMLR, pp 6944\u20136959"},{"key":"1031_CR29","first-page":"3315","volume":"29","author":"M Hardt","year":"2016","unstructured":"Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Adv Neural Inf Process Syst 29:3315\u20133323","journal-title":"Adv Neural Inf Process Syst"},{"key":"1031_CR30","unstructured":"Impact ldp on fairness repository (2023). https:\/\/github.com\/KarimaMakhlouf\/Impact_of_LDP_on_Fairness"},{"key":"1031_CR31","unstructured":"Jagielski M, Kearns M, Mao J, Oprea A, Roth A, Sharifi-Malvajerdi S, Ullman J (2019) Differentially private fair learning. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th international conference on machine learning, volume\u00a097 of proceedings of machine learning research. PMLR, 09\u201315 Jun, pp 3000\u20133008"},{"key":"1031_CR32","unstructured":"Kairouz P, Bonawitz K, Ramage D (2016) Discrete distribution estimation under local privacy. In: International conference on machine learning. PMLR, pp 2436\u20132444"},{"issue":"3","key":"1031_CR33","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1137\/090756090","volume":"40","author":"SP Kasiviswanathan","year":"2011","unstructured":"Kasiviswanathan SP, Lee HK, Nissim K (2011) What can we learn privately? SIAM J. Comput. 40(3):793\u2013826","journal-title":"SIAM J. Comput."},{"key":"1031_CR34","unstructured":"Kikuchi H (2022) Castell: scalable joint probability estimation of multi-dimensional data randomized with local differential privacy. arXiv preprint arXiv:2212.01627"},{"key":"1031_CR35","unstructured":"Liu G, Tang P, Hu C, Jin C, Guo S (2023) Multi-dimensional data publishing with local differential privacy. In Proceedings of the 26th international conference on extending database technology, EDBT 2023, Ioannina, Greece, March 28\u201331, 2023, pp 183\u2013194. OpenProceedings.org"},{"issue":"5","key":"1031_CR36","doi-asserted-by":"publisher","first-page":"102642","DOI":"10.1016\/j.ipm.2021.102642","volume":"58","author":"K Makhlouf","year":"2021","unstructured":"Makhlouf K, Zhioua S, Palamidessi C (2021) Machine learning fairness notions: bridging the gap with real-world applications. Inf. Process. Manag. 58(5):102642","journal-title":"Inf. Process. Manag."},{"key":"1031_CR37","doi-asserted-by":"crossref","unstructured":"Makhlouf K, Zhioua S, Palamidessi C (2021) On the applicability of machine learning fairness notions. 23(1):14\u201323","DOI":"10.1145\/3468507.3468511"},{"key":"1031_CR38","doi-asserted-by":"crossref","unstructured":"Makhlouf K, Zhioua S, Palamidessi C (2022) Identifiability of causal-based ml fairness notions. In: 2022 14th international conference on computational intelligence and communication networks (CICN), pp 1\u20138","DOI":"10.1109\/CICN56167.2022.10008263"},{"key":"1031_CR39","unstructured":"Mangold P, Perrot M, Bellet A, Tommasi M (2023) Differential privacy has bounded impact on fairness in classification. In: Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J (eds) Proceedings of the 40th international conference on machine learning, volume 202 of proceedings of machine learning research. PMLR, 23\u201329, pp 23681\u201323705"},{"issue":"6","key":"1031_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2021) A survey on bias and fairness in machine learning. ACM Comput Surv 54(6):1\u201335","journal-title":"ACM Comput Surv"},{"key":"1031_CR41","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1146\/annurev-statistics-042720-125902","volume":"8","author":"S Mitchell","year":"2021","unstructured":"Mitchell S, Potash E, Barocas S, D\u2019Amour A, Lum K (2021) Algorithmic fairness: choices, assumptions, and definitions. Ann Rev Stat Appl 8:141\u2013163","journal-title":"Ann Rev Stat Appl"},{"key":"1031_CR42","unstructured":"Mozannar H, Ohannessian M, Srebro N (2020) Fair learning with private demographic data. In: International conference on machine learning. PMLR, pp 7066\u20137075"},{"key":"1031_CR43","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"J Pearl","year":"2009","unstructured":"Pearl J (2009) Causality. Cambridge University Press, Cambridge"},{"key":"1031_CR44","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J (2011) Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12:2825\u20132830","journal-title":"J. Mach. Learn. Res."},{"issue":"9","key":"1031_CR45","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1109\/TIFS.2018.2812146","volume":"13","author":"X Ren","year":"2018","unstructured":"Ren X, Yu CM, Yu W, Yang S, Yang X, McCann JA, Philip SY (2018) LoPub: high-dimensional crowdsourced data publication with local differential privacy. IEEE Trans. Inf. Forens. Secur. 13(9):2151\u20132166","journal-title":"IEEE Trans. Inf. Forens. Secur."},{"key":"1031_CR46","doi-asserted-by":"crossref","unstructured":"Tran C, Fioretto F, Van Hentenryck P (2021) Differentially private and fair deep learning: a Lagrangian dual approach. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, no 11, pp 9932\u20139939","DOI":"10.1609\/aaai.v35i11.17193"},{"key":"1031_CR47","doi-asserted-by":"crossref","unstructured":"Verma S, Rubin J (2018) Fairness definitions explained. In: 2018 IEEE\/ACM international workshop on software fairness (FairWare). IEEE, pp 1\u20137","DOI":"10.1145\/3194770.3194776"},{"key":"1031_CR48","unstructured":"Wang T, Blocki J, Li N, Jha S (2017) Locally differentially private protocols for frequency estimation. In: 26th USENIX security symposium (USENIX Security 17). USENIX Association, Vancouver, BC, pp 729\u2013745"},{"issue":"309","key":"1031_CR49","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1080\/01621459.1965.10480775","volume":"60","author":"SL Warner","year":"1965","unstructured":"Warner SL (1965) Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309):63\u201369","journal-title":"J. Am. Stat. Assoc."},{"key":"1031_CR50","doi-asserted-by":"crossref","unstructured":"Xu D, Yuan S, Wu X (2019) Achieving differential privacy and fairness in logistic regression. In: Companion proceedings of The 2019 world wide web conference, pp 594\u2013599","DOI":"10.1145\/3308560.3317584"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-024-01031-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-024-01031-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-024-01031-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T10:40:54Z","timestamp":1722336054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-024-01031-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":50,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["1031"],"URL":"https:\/\/doi.org\/10.1007\/s10618-024-01031-0","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2024,5,27]]},"assertion":[{"value":"6 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}