Differential private relevance learning

Brinkrolf J, Berger K, Hammer B (2018)
In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Verleysen M (Ed); 555-560.

Konferenzbeitrag | Veröffentlicht | Englisch
 
Herausgeber*in
Verleysen, Michel
Abstract / Bemerkung
Digital information is collected daily in growing volumes. Mutual benefits drive the demand for the exchange and publication of data among parties. However, it is often unclear how to handle these data properly in the case that the data contains sensitive information. Differ- ential privacy has become a powerful principle for privacy-preserving data analysis tasks in the last few years, since it entails a formal privacy guar- antee for such settings. This is obtained by a separation of the utility of the database and the risk of an individual to lose his/her privacy. In this contribution, we introduce the Laplace mechanism and a stochastic gradient descent methodology which guarantee differential privacy [1]. Then, we show how these paradigms can be incorporated into two popular ma- chine learning algorithm, namely GLVQ and GMLVQ. We demonstrate the results of privacy-preserving LVQ based on three benchmarks.
Erscheinungsjahr
2018
Titel des Konferenzbandes
Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
Seite(n)
555-560
Konferenz
26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Konferenzort
Bruges (Belgium)
Konferenzdatum
2018-04-25 – 2018-04-27
ISBN
978-287587047-6
Page URI
https://pub.uni-bielefeld.de/record/2918254

Zitieren

Brinkrolf J, Berger K, Hammer B. Differential private relevance learning. In: Verleysen M, ed. Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). 2018: 555-560.
Brinkrolf, J., Berger, K., & Hammer, B. (2018). Differential private relevance learning. In M. Verleysen (Ed.), Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) (pp. 555-560).
Brinkrolf, J., Berger, K., and Hammer, B. (2018). “Differential private relevance learning” in Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Verleysen, M. ed. 555-560.
Brinkrolf, J., Berger, K., & Hammer, B., 2018. Differential private relevance learning. In M. Verleysen, ed. Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). pp. 555-560.
J. Brinkrolf, K. Berger, and B. Hammer, “Differential private relevance learning”, Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), M. Verleysen, ed., 2018, pp.555-560.
Brinkrolf, J., Berger, K., Hammer, B.: Differential private relevance learning. In: Verleysen, M. (ed.) Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). p. 555-560. (2018).
Brinkrolf, Johannes, Berger, Kolja, and Hammer, Barbara. “Differential private relevance learning”. Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Ed. Michel Verleysen. 2018. 555-560.
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