Graduate School and Research Center in Digital Sciences

Privacy preserving neural network classi cation: A hybrid solution

Tillem, Gamze; Bozdemir, Beyza; Önen, Melek; Ermis, Orhan

PUT 2019, Open Day for Privacy, Usability, and Transparency, Co-located with the 19th Privacy Enhancing Technologies Symposium, July 15, 2019, KTH, Stockholm, Sweden

Best Poster Demo Award

Document Bibtex

Title:Privacy preserving neural network classi cation: A hybrid solution
Keywords:Privacy, Neural Network, Homomorphic encryption, Secure two-party computation
Type:Poster / Demo
Language:English
City:Stockholm
Country:SWEDEN
Date:
Department:Digital Security
Eurecom ref:5955
Copyright: © Springer. Personal use of this material is permitted. The definitive version of this paper was published in PUT 2019, Open Day for Privacy, Usability, and Transparency, Co-located with the 19th Privacy Enhancing Technologies Symposium, July 15, 2019, KTH, Stockholm, Sweden and is available at :
Bibtex: @poster / demo{EURECOM+5955, year = {2019}, title = {{P}rivacy preserving neural network classi cation: {A} hybrid solution}, author = {{T}illem, {G}amze and {B}ozdemir, {B}eyza and {\"{O}}nen, {M}elek and {E}rmis, {O}rhan}, number = {EURECOM+5955}, month = {07}, institution = {Eurecom} address = {{S}tockholm, {SWEDEN}}, url = {http://www.eurecom.fr/publication/5955} }
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