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Approximating Robust Linear Regression With An Integral Privacy Guarantee
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0002-2564-0683
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0002-0368-8037
2018 (engelsk)Inngår i: 2018 16th Annual Conference on Privacy, Security and Trust (PST) / [ed] Kieran McLaughlin, Ali Ghorbani, Sakir Sezer, Rongxing Lu, Liqun Chen, Robert H. Deng, Paul Miller, Stephen Marsh, Jason Nurse, IEEE, 2018, s. 85-94Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Most of the privacy-preserving techniques suffer from an inevitable utility loss due to different perturbations carried out on the input data or the models in order to gain privacy. When it comes to machine learning (ML) based prediction models, accuracy is the key criterion for model selection. Thus, an accuracy loss due to privacy implementations is undesirable. The motivation of this work, is to implement the privacy model "integral privacy" and to evaluate its eligibility as a technique for machine learning model selection while preserving model utility. In this paper, a linear regression approximation method is implemented based on integral privacy which ensures high accuracy and robustness while maintaining a degree of privacy for ML models. The proposed method uses a re-sampling based estimator to construct linear regression model which is coupled with a rounding based data discretization method to support integral privacy principles. The implementation is evaluated in comparison with differential privacy in terms of privacy, accuracy and robustness of the output ML models. In comparison, integral privacy based solution provides a better solution with respect to the above criteria.

sted, utgiver, år, opplag, sider
IEEE, 2018. s. 85-94
Serie
Annual Conference on Privacy Security and Trust-PST, ISSN 1712-364X
Emneord [en]
Integral privacy, Linear regression, Privacy-preserving machine learning
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF303 Informationssäkerhet
Identifikatorer
URN: urn:nbn:se:his:diva-16573DOI: 10.1109/PST.2018.8514161ISI: 000454683600008Scopus ID: 2-s2.0-85063441298ISBN: 978-1-5386-7494-9 (tryckt)ISBN: 978-1-5386-7493-2 (digital)OAI: oai:DiVA.org:his-16573DiVA, id: diva2:1280307
Konferanse
16th Annual Conference on Privacy, Security and Trust (PST), Belfast, Northern Ireland, August 28-30, 2018
Tilgjengelig fra: 2019-01-18 Laget: 2019-01-18 Sist oppdatert: 2019-07-10bibliografisk kontrollert

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