Privacy-preserving Machine Learning in Cloud
Author:
Affiliation:
1. University of North Texas, Denton, TX, USA
2. University of Saskatchewan, Saskatchewan, Canada
3. Meredith College, Raleigh, NC, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3140649.3140655
Reference20 articles.
1. Louis J. M. Aslett Pedro M. Esperança and Chris C. Holmes. 2015. Encrypted statistical machine learning: new privacy preserving methods. CoRR Vol. abs/1508.06845 (2015). Louis J. M. Aslett Pedro M. Esperança and Chris C. Holmes. 2015. Encrypted statistical machine learning: new privacy preserving methods. CoRR Vol. abs/1508.06845 (2015).
2. L. J. M. Aslett P. M. Esperança and C. C. Holmes. 2015. A review of homomorphic encryption and software tools for encrypted statistical machine learning. Technical Report. University of Oxford. L. J. M. Aslett P. M. Esperança and C. C. Holmes. 2015. A review of homomorphic encryption and software tools for encrypted statistical machine learning. Technical Report. University of Oxford.
3. Raphael Bost Raluca Ada Popa Stephen Tu and Shafi Goldwasser. 2015. Machine Learning Classification over Encrypted Data 22nd Annual Network and Distributed System Security Symposium NDSS San Diego California USA. Raphael Bost Raluca Ada Popa Stephen Tu and Shafi Goldwasser. 2015. Machine Learning Classification over Encrypted Data 22nd Annual Network and Distributed System Security Symposium NDSS San Diego California USA.
4. Google 2017. Google Prediction API. (2017). https://cloud.google.com/prediction/ Google 2017. Google Prediction API. (2017). https://cloud.google.com/prediction/
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