Biblio

Machine Learning as a Service (MLaaS) is a growing paradigm in the Machine Learning (ML) landscape. More and more ML models are being uploaded to the cloud and made accessible from all over the world. Creating good ML models, however, can be expensive and the used data is often sensitive. Recently, Secure Multi-Party Computation (SMPC) protocols for MLaaS have been proposed, which protect sensitive user data and ML models at the expense of substantially higher computation and communication than plaintext evaluation. In this paper, we show that for a subset of ML models used in MLaaS, namely Support Vector Machines (SVMs) and Support Vector Regression Machines (SVRs) which have found many applications to classifying multimedia data such as texts and images, it is possible for adversaries to passively extract the private models even if they are protected by SMPC, using known and newly devised model extraction attacks. We show that our attacks are not only theoretically possible but also practically feasible and cheap, which makes them lucrative to financially motivated attackers such as competitors or customers. We perform model extraction attacks on the homomorphic encryption-based protocol for privacy-preserving SVR-based indoor localization by Zhang et al. (International Workshop on Security 2016). We show that it is possible to extract a highly accurate model using only 854 queries with the estimated cost of \$0.09 on the Amazon ML platform, and our attack would take only 7 minutes over the Internet. Also, we perform our model extraction attacks on SVM and SVR models trained on publicly available state-of-the-art ML datasets.

Internet eXchange Points (IXPs) play an ever-growing role in Internet inter-connection. To facilitate the exchange of routes amongst their members, IXPs provide Route Server (RS) services to dispatch the routes according to each member's peering policies. Nowadays, to make use of RSes, these policies must be disclosed to the IXP. This poses fundamental questions regarding the privacy guarantees of route-computation on confidential business information. Indeed, as evidenced by interaction with IXP administrators and a survey of network operators, this state of affairs raises privacy concerns among network administrators and even deters some networks from subscribing to RS services. We design Sixpack1, an RS service that leverages Secure Multi-Party Computation (SMPC) to keep peering policies confidential, while extending, the functionalities of today's RSes. As SMPC is notoriously heavy in terms of communication and computation, our design and implementation of Sixpack aims at moving computation outside of the SMPC without compromising the privacy guarantees. We assess the effectiveness and scalability of our system by evaluating a prototype implementation using traces of data from one of the largest IXPs in the world. Our evaluation results indicate that Sixpack can scale to support privacy-preserving route-computation, even at IXPs with many hundreds of member networks.