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4 papers · 1 filter
Machine Learning Models that Remember Too Much
Congzheng Song, Thomas Ristenpart, Vitaly Shmatikov
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their…
Plausible Deniability for Privacy-Preserving Data Synthesis
Vincent Bindschaedler, Reza Shokri, Carl A. Gunter
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic…
Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels +2
Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML…
A Placement Vulnerability Study in Multi-tenant Public Clouds
Venkatanathan Varadarajan, Yinqian Zhang, Thomas Ristenpart +1
Public infrastructure-as-a-service clouds, such as Amazon EC2, Google Compute Engine (GCE) and Microsoft Azure allow clients to run virtual machines (VMs) on shared physical infras…