{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:08:12Z","timestamp":1777900092125,"version":"3.51.4"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032060952","type":"print"},{"value":"9783032060969","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-06096-9_15","type":"book-chapter","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T09:54:14Z","timestamp":1758880454000},"page":"259-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["P2NIA: Privacy-Preserving Non-iterative Auditing"],"prefix":"10.1007","author":[{"given":"Jade Garcia","family":"Bourr\u00e9e","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadrien","family":"Lautraite","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00e9bastien","family":"Gambs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gilles","family":"Tredan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erwan","family":"Le Merrer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beno\u00eet","family":"Rottembourg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,27]]},"reference":[{"key":"15_CR1","unstructured":"Alaa, A., Van\u00a0Breugel, B., Saveliev, E.S., Van Der\u00a0Schaar, M.: How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models. In: International Conference on Machine Learning. PMLR (2022)"},{"key":"15_CR2","unstructured":"American city and council: Avoiding bumps in the road when designing ai-powered traffic management systems. https:\/\/www.americancityandcounty.com\/2024\/01\/26\/avoiding-bumps-in-the-road-when-designing-ai-powered-traffic-management-systems\/"},{"key":"15_CR3","unstructured":"Apple Inc.: Differential privacy overview (2017). https:\/\/www.apple.com\/privacy\/docs\/Differential_Privacy_Overview.pdf"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Bandy, J.: Problematic machine behavior: a systematic literature review of algorithm audits. In: Proceedings of the ACM on Human-Computer Interaction (2021)","DOI":"10.1145\/3449148"},{"key":"15_CR5","unstructured":"Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning: Limitations and Opportunities. MIT Press, Cambridge (2023)"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Bassily, R., Smith, A.: Local, private, efficient protocols for succinct histograms. In: Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing, pp. 127\u2013135 (2015)","DOI":"10.1145\/2746539.2746632"},{"key":"15_CR7","unstructured":"BBC: Ai hiring tools may be filtering out the best job applicants. https:\/\/www.bbc.com\/worklife\/article\/20240214-ai-recruiting-hiring-software-bias-discrimination"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Becker, B., Kohavi, R.: Adult. UCI Mach. Learn. Repository (1996). https:\/\/doi.org\/10.24432\/C5XW20","DOI":"10.24432\/C5XW20"},{"issue":"2","key":"15_CR9","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1080\/00031305.2021.1952897","volume":"76","author":"P Besse","year":"2022","unstructured":"Besse, P., del Barrio, E., Gordaliza, P., Loubes, J.M., Risser, L.: A survey of bias in machine learning through the prism of statistical parity. Am. Stat. 76(2), 188\u2013198 (2022)","journal-title":"Am. Stat."},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Birhane, A., Steed, R., Ojewale, V., Vecchione, B., Raji, I.D.: AI auditing: the broken bus on the road to AI accountability. In: 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), pp. 612\u2013643. IEEE (2024)","DOI":"10.1109\/SaTML59370.2024.00037"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Casper, S., et\u00a0al.: Black-box access is insufficient for rigorous AI audits. In: The 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 2254\u20132272 (2024)","DOI":"10.1145\/3630106.3659037"},{"key":"15_CR12","unstructured":"CBS News: Argentina plans to use ai to \u201cpredict future crimes and help prevent them\u201d. https:\/\/www.cbsnews.com\/news\/argentina-plans-to-use-ai-to-predict-future-crimes-and-help-prevent-them\/"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Chaudhuri, A., Mukerjee, R.: Randomized Response: Theory and Techniques. Routledge (2020)","DOI":"10.1201\/9780203741290"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Chen, L., Mislove, A., Wilson, C.: An empirical analysis of algorithmic pricing on amazon marketplace. In: Proceedings of the 25th International Conference on World Wide Web, pp. 1339\u20131349 (2016)","DOI":"10.1145\/2872427.2883089"},{"key":"15_CR15","unstructured":"Cochran, W.G.: Sampling Techniques. Wiley, Hoboken (1977)"},{"key":"15_CR16","unstructured":"Ding, F., Hardt, M., Miller, J., Schmidt, L.: Retiring adult: new datasets for fair machine learning. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"issue":"10","key":"15_CR17","doi-asserted-by":"publisher","first-page":"4933","DOI":"10.1109\/TKDE.2020.3045759","volume":"34","author":"J Domingo-Ferrer","year":"2020","unstructured":"Domingo-Ferrer, J., Soria-Comas, J.: Multi-dimensional randomized response. IEEE Trans. Knowl. Data Eng. 34(10), 4933\u20134946 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Duddu, V., Das, A., Khayata, N., Yalame, H., Schneider, T., Asokan, N.: Attesting distributional properties of training data for machine learning. In: European Symposium on Research in Computer Security, pp. 3\u201323. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-70879-4_1"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Dunna, A., Keith, K.A., Zuckerman, E., Vallina-Rodriguez, N., O\u2019Connor, B., Nithyanand, R.: Paying attention to the algorithm behind the curtain: Bringing transparency to youtube\u2019s demonetization algorithms. In: Proceedings of the ACM on Human-Computer Interaction, vol. 6(CSCW2), pp. 1\u201331 (2022)","DOI":"10.1145\/3555209"},{"issue":"1","key":"15_CR20","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1145\/1866739.1866758","volume":"54","author":"C Dwork","year":"2011","unstructured":"Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86\u201395 (2011)","journal-title":"Commun. ACM"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214\u2013226 (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Dwork, C., Roth, A., et\u00a0al.: The algorithmic foundations of differential privacy. Found. Trends\u00ae Theor. Comput. Sci. (2014)","DOI":"10.1561\/9781601988195"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Erlingsson, \u00da., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1054\u20131067 (2014)","DOI":"10.1145\/2660267.2660348"},{"key":"15_CR24","unstructured":"European Union: Artificial intelligence act (2024). http:\/\/data.europa.eu\/eli\/reg\/2024\/1689\/oj"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. In: proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 259\u2013268 (2015)","DOI":"10.1145\/2783258.2783311"},{"issue":"15","key":"15_CR26","doi-asserted-by":"publisher","first-page":"2733","DOI":"10.3390\/math10152733","volume":"10","author":"A Figueira","year":"2022","unstructured":"Figueira, A., Vaz, B.: Survey on synthetic data generation, evaluation methods and GANs. Mathematics 10(15), 2733 (2022)","journal-title":"Mathematics"},{"key":"15_CR27","unstructured":"Financial Times: Volatility: how \u2018algos\u2019 changed the rhythm of the market. https:\/\/www.ft.com\/content\/fdc1c064-1142-11e9-a581-4ff78404524e"},{"key":"15_CR28","unstructured":"Forbes: Sports AI can be a game-changing partner for coaches. https:\/\/www.forbes.com\/sites\/geristengel\/2024\/05\/15\/sports-ai-can-be-a-game-changing-partner-for-coaches\/"},{"key":"15_CR29","unstructured":"French Government: Penal code - article 434-10 (2020). https:\/\/www.legifrance.gouv.fr\/codes\/article_lc\/LEGIARTI000042026716\/. Accessed 20 Aug 2024"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Fukuchi, K., Hara, S., Maehara, T.: Faking fairness via stealthily biased sampling. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i01.5377"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Garcia\u00a0Bourr\u00e9e, J., Lautraite, H., Gambs, S., Tredan, G., Le\u00a0Merrer, E., Rottembourg, B.: P2nia: privacy-preserving non-iterative auditing. arXiv preprint arXiv:2504.00874 (2025)","DOI":"10.1007\/978-3-032-06096-9_15"},{"key":"15_CR32","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"issue":"1","key":"15_CR33","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1177\/0002716215570866","volume":"659","author":"E Hargittai","year":"2015","unstructured":"Hargittai, E.: Is bigger always better? potential biases of big data derived from social network sites. Ann. Am. Acad. Pol. Soc. Sci. 659(1), 63\u201376 (2015)","journal-title":"Ann. Am. Acad. Pol. Soc. Sci."},{"key":"15_CR34","unstructured":"Harvard Business review: Algorithms can save networking from being business card roulette. https:\/\/hbr.org\/2014\/03\/algorithms-can-save-networking-from-being-business-card-roulette"},{"key":"15_CR35","doi-asserted-by":"publisher","first-page":"31045","DOI":"10.52202\/068431-2251","volume":"35","author":"M Kang","year":"2022","unstructured":"Kang, M., Li, L., Weber, M., Liu, Y., Zhang, C., Li, B.: Certifying some distributional fairness with subpopulation decomposition. Adv. Neural. Inf. Process. Syst. 35, 31045\u201331058 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR36","unstructured":"Kilbertus, N., Gasc\u00f3n, A., Kusner, M., Veale, M., Gummadi, K., Weller, A.: Blind justice: fairness with encrypted sensitive attributes. In: International Conference on Machine Learning, pp. 2630\u20132639. PMLR (2018)"},{"key":"15_CR37","doi-asserted-by":"crossref","unstructured":"Le\u00a0Merrer, E., Pons, R., Tr\u00e9dan, G.: Algorithmic audits of algorithms, and the law. AI Ethics, 1\u201311 (2023)","DOI":"10.2139\/ssrn.4232505"},{"key":"15_CR38","doi-asserted-by":"crossref","unstructured":"Makhlouf, K., Stefanovi\u0107, T., Arcolezi, H.H., Palamidessi, C.: A systematic and formal study of the impact of local differential privacy on fairness: Preliminary results. In: IEEE 37th Computer Security Foundations Symposium, pp. 1\u201316 (2024)","DOI":"10.1109\/CSF61375.2024.00039"},{"key":"15_CR39","unstructured":"McKenna, R., Miklau, G., Sheldon, D.: Private-pgm (2021). https:\/\/github.com\/journalprivacyconfidentiality\/private-pgm-jpc-778\/tree\/v2021-10-04-jpc"},{"key":"15_CR40","doi-asserted-by":"crossref","unstructured":"McKenna, R., Miklau, G., Sheldon, D.: Winning the NIST contest: a scalable and general approach to differentially private synthetic data. arXiv preprint arXiv:2108.04978 (2021)","DOI":"10.29012\/jpc.778"},{"key":"15_CR41","unstructured":"McKenna, R., Mullins, B., Sheldon, D., Miklau, G.: Aim: an adaptive and iterative mechanism for differentially private synthetic data. arXiv preprint arXiv:2201.12677 (2022)"},{"key":"15_CR42","unstructured":"McKenna, R., Sheldon, D., Miklau, G.: Graphical-model based estimation and inference for differential privacy. In: International Conference on Machine Learning, pp. 4435\u20134444. PMLR (2019)"},{"issue":"6","key":"15_CR43","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.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 1\u201335 (2021)","journal-title":"ACM Comput. Surv."},{"issue":"5","key":"15_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102657","volume":"58","author":"C Panigutti","year":"2021","unstructured":"Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., Pedreschi, D.: FairLens: auditing black-box clinical decision support systems. Inf. Process. Manage. 58(5), 102657 (2021)","journal-title":"Inf. Process. Manage."},{"key":"15_CR45","unstructured":"Pentyala, S., Melanson, D., De\u00a0Cock, M., Farnadi, G.: Privfair: a library for privacy-preserving fairness auditing. arXiv preprint arXiv:2202.04058 (2022)"},{"key":"15_CR46","unstructured":"Sandvig, C., Hamilton, K., Karahalios, K., Langbort, C.: Auditing algorithms: research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry (2014)"},{"key":"15_CR47","unstructured":"Shamsabadi, A.S., et al.: Confidential-profitt: confidential proof of fair training of trees. In: ICLR (2022)"},{"key":"15_CR48","doi-asserted-by":"crossref","unstructured":"Silva, M., Santos\u00a0de Oliveira, L., Andreou, A., Vaz\u00a0de Melo, P.O., Goga, O., Benevenuto, F.: Facebook ads monitor: an independent auditing system for political ads on Facebook. In: Proceedings of the Web Conference 2020, pp. 224\u2013234 (2020)","DOI":"10.1145\/3366423.3380109"},{"key":"15_CR49","unstructured":"Tao, Y., McKenna, R., Hay, M., Machanavajjhala, A., Miklau, G.: Benchmarking differentially private synthetic data generation algorithms. arXiv preprint arXiv:2112.09238 (2021)"},{"key":"15_CR50","doi-asserted-by":"crossref","unstructured":"Taskesen, B., Blanchet, J., Kuhn, D., Nguyen, V.A.: A statistical test for probabilistic fairness. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 648\u2013665 (2021)","DOI":"10.1145\/3442188.3445927"},{"key":"15_CR51","unstructured":"The Guardian: Ai better than biopsy at assessing some cancers, study finds. https:\/\/www.bbc.com\/worklife\/article\/20240214-ai-recruiting-hiring-softwar-bias-discrimination"},{"key":"15_CR52","doi-asserted-by":"crossref","unstructured":"Toreini, E., Mehrnezhad, M., Van\u00a0Moorsel, A.: Verifiable fairness: privacy\u2013preserving computation of fairness for machine learning systems. In: European Symposium on Research in Computer Security, pp. 569\u2013584. Springer (2023)","DOI":"10.1007\/978-3-031-54129-2_34"},{"key":"15_CR53","doi-asserted-by":"crossref","unstructured":"de\u00a0Vos, M., et al.: Fairness auditing with multi-agent collaboration. In: ECAI 2024, pp. 1116\u20131123. IOS Press (2024)","DOI":"10.3233\/FAIA240604"},{"key":"15_CR54","unstructured":"Wang, S., et al.: Privacy amplification via shuffling: unified, simplified, and tightened. arXiv preprint arXiv:2304.05007 (2023)"},{"key":"15_CR55","unstructured":"Wang, T., Blocki, J., Li, N., Jha, S.: Locally differentially private protocols for frequency estimation. In: 26th USENIX Security Symposium, pp. 729\u2013745 (2017)"},{"issue":"309","key":"15_CR56","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":"15_CR57","doi-asserted-by":"crossref","unstructured":"Ye, M., Barg, A.: Optimal schemes for discrete distribution estimation under locally differential privacy. IEEE Trans. Inf. Theory (2018)","DOI":"10.1109\/TIT.2018.2809790"},{"key":"15_CR58","unstructured":"Yuan, C.C.R., Wang, B.Y.: Quantitative auditing of AI fairness with differentially private synthetic data. arXiv preprint arXiv:2504.21634 (2025)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06096-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:23:16Z","timestamp":1777630996000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06096-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,27]]},"ISBN":["9783032060952","9783032060969"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06096-9_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,27]]},"assertion":[{"value":"27 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}