Biblio

The increasing data generation rate and the proliferation of deep learning applications have led to the development of machine learning-as-a-service (MLaaS) platforms by major Cloud providers. The existing MLaaS platforms, however, fall short in protecting the clients’ private data. Recent distributed MLaaS architectures such as federated learning have also shown to be vulnerable against a range of privacy attacks. Such vulnerabilities motivated the development of privacy-preserving MLaaS techniques, which often use complex cryptographic prim-itives. Such approaches, however, demand abundant computing resources, which undermine the low-latency nature of evolving applications such as autonomous driving.To address these challenges, we propose SCLERA–an efficient MLaaS framework that utilizes trusted execution environment for secure execution of clients’ workloads. SCLERA features a set of optimization techniques to reduce the computational complexity of the offloaded services and achieve low-latency inference. We assessed SCLERA’s efficacy using image/video analytic use cases such as scene detection. Our results show that SCLERA achieves up to 23× speed-up when compared to the baseline secure model execution.

The increased need for online account security and the prominence of smartphones in today’s society has led to smartphone-based two-factor authentication schemes, in which the second factor is a code received on the user’s smartphone. Evolving two-factor authentication mechanisms suggest using the proximity of the user’s devices as the second authentication factor, avoiding the inconvenience of user-device interaction. These mechanisms often use low-range communication technologies or the similarities of devices’ environments to prove devices’ proximity and user authenticity. However, such mechanisms are vulnerable to colocated adversaries. This paper proposes Vibe-an implicit two-factor authentication mechanism, which uses a vibration communication channel to prove users’ authenticity in a secure and non-intrusive manner. Vibe’s design provides security at the physical layer, reducing the attack surface to the physical surface shared between devices. As a result, it protects users’ security even in the presence of co-located adversaries-the primary drawback of the existing systems. We prototyped Vibe and assessed its performance using commodity hardware in different environments. Our results show an equal error rate of 0.0175 with an end-to-end authentication latency of approximately 3.86 seconds.

The shift from the host-centric to the information-centric paradigm results in many benefits including native security, enhanced mobility, and scalability. The corresponding information-centric networking (ICN), also presents several important challenges, such as closest replica routing, client privacy, and client preference collection. The majority of these challenges have received the research community’s attention. However, no mechanisms have been proposed for the challenge of effective client preferences collection. In the era of big data analytics and recommender systems customer preferences are essential for providers such as Amazon and Netflix. However, with content served from in-network caches, the ICN paradigm indirectly undermines the gathering of these essential individualized preferences. In this paper, we discuss the requirements for client preference collections and present potential mechanisms that may be used for achieving it successfully.

The shift from the host-centric to the information-centric paradigm results in many benefits including native security, enhanced mobility, and scalability. The corresponding information-centric networking (ICN), also presents several important challenges, such as closest replica routing, client privacy, and client preference collection. The majority of these challenges have received the research community’s attention. However, no mechanisms have been proposed for the challenge of effective client preferences collection. In the era of big data analytics and recommender systems customer preferences are essential for providers such as Amazon and Netflix. However, with content served from in-network caches, the ICN paradigm indirectly undermines the gathering of these essential individualized preferences. In this paper, we discuss the requirements for client preference collections and present potential mechanisms that may be used for achieving it successfully.