Related papers: Privacy-preserving Network Functionality Outsourci…
Recent years witnessed a surge in network traffic due to the emergence of new online services, causing periodic saturation and complexity problems. Additionally, the growing number of IoT devices further compounds the problem. Software…
Network tomography plays a crucial role in network monitoring and management, where network topology serves as the fundamental basis for various tomography tasks including traffic matrix estimation and link performance inference. The…
5G mobile networks provide additional benefits in terms of lower latency, higher data rates, and more coverage, in comparison to 4G networks, and they are also coming close to standardization. For example, 5G has a new level of data…
Software Defined Networking (SDN) is based basically on three features: centralization of the control plane, programmability of network functions and traffic engineering. The network function migration poses interesting problems that we try…
Traditional network resident functions (e.g., firewalls, network address translation) and middleboxes (caches, load balancers) have moved from purpose-built appliances to software-based components. However, L2/L3 network functions (NFs) are…
The huge computation demand of deep learning models and limited computation resources on the edge devices calls for the cooperation between edge device and cloud service by splitting the deep models into two halves. However, transferring…
Recent SDN-based solutions give cloud providers the opportunity to extend their "as-a-service" model with the offer of complete network virtualization. They provide tenants with the freedom to specify the network topologies and addressing…
The remarkable success of machine learning has fostered a growing number of cloud-based intelligent services for mobile users. Such a service requires a user to send data, e.g. image, voice and video, to the provider, which presents a…
Compressed sensing (CS), breaking the constriction of Shannon-Nyquist sampling theorem, is a very promising data acquisition technique in the era of multimedia big data. However, the high complexity of CS reconstruction algorithm is a big…
In modern networks, forwarding of packets often depends on the history of previously transmitted traffic. Such networks contain stateful middleboxes, whose forwarding behaviour depends on a mutable internal state. Firewalls and load…
Stateful Middleboxes are integral part of enterprise and campus networks that provide essential in-network, security, and value-added services. These stateful middleboxes rely on precise network flow identification. However, the adoption of…
The increasing popularity of online social network brings huge privacy threat for the end users. While existing work focus on inferring sensitive attributes from the social network such as age, location and gender, little has been done on…
Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture…
Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services but suffer from…
Software-Defined-eXchanges (SDXes) promise to tackle the timely quest of bringing improving the inter-domain routing ecosystem through SDN deployment. Yet, the naive deployment of SDN on the Internet raises concerns about the correctness of…
The emergence of cloud computing provides a new computing paradigm for users -- massive and complex computing tasks can be outsourced to cloud servers. However, the privacy issues also follow. Fully homomorphic encryption shows great…
Cloud computing enables users to process and store data remotely on high-performance computers and servers by sharing data over the Internet. However, transferring data to clouds causes unavoidable privacy concerns. Here, we present a…
Utilization of Machine Learning (ML) algorithms, especially Deep Neural Network (DNN) models, becomes a widely accepted standard in many domains more particularly IoT-based systems. DNN models reach impressive performances in several…
This white paper presents an analysis done by the MAMI project of the privacy and security concerns surrounding middlebox cooperation protocols (MCPs), based on our experimental experience with the Path Layer UDP Substrate (PLUS) proposal.…
The vast storage capacity and computational power of cloud servers have led to the widespread outsourcing of machine learning inference services. While offering significant operational benefits, this practice also introduces privacy risks,…