相关论文: Towards a Tectonic Traffic Shift? Investigating Ap…
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,…
The ATLAS detector at CERN has completed its first full year of recording collisions at 7 TeV, resulting in billions of events and petabytes of data. At these scales, physicists must have the capability to read only the data of interest to…
Connected Vehicles network is designed to provide a secure and private method for drivers to use the most efficiently the roads in certain area. When dealing with the scenario of car to access points connectivity (Wi-Fi, 3G, LTE), the…
In structured peer-to-peer networks, like Chord, users find data by asking a number of intermediate nodes in the network. Each node provides the identity of the closet known node to the address of the data, until eventually the node…
Differentially private wireless federated learning (DPWFL) is a promising framework for protecting sensitive user data. However, foundational questions on how to precisely characterize privacy loss remain open, and existing work is further…
The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich…
Public blockchains, though renowned for their transparency and immutability, suffer from significant privacy concerns. Network-level analysis and long-term observation of publicly available transactions can often be used to infer user…
This paper presents Wally, a private search system that supports efficient search queries against large databases. When sufficiently many clients are making queries, Wally's performance is significantly better than previous systems while…
We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy. Motivated by stochastic optimization and the federated…
In heterogeneous dense networks where spectrum is shared, users privacy remains one of the major challenges. On a multi-antenna relay-assisted multi-carrier interference channel, each user shares the frequency and spatial resources with all…
In today's data centers, the performance of interconnects plays a pivotal role. However, many of the underlying technologies for these interconnects have a history of several decades and existed long before data centers came into being.To…
Network traffic classification has been widely studied to fundamentally advance network measurement and management. Machine Learning is one of the effective approaches for network traffic classification. Specifically, Deep Learning (DL) has…
Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation. These devices are used for real-world applications such as sensor monitoring, real-time…
The recent introduction of Find My app by Apple will open a large window of opportunities for whistleblowers. Based on a short range Bluetooth signals, an EC P-224 encryption, and an end-to-end encrypted manner using iCloud Keychain, Find…
We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art of private deep learning…
Data privacy is of great concern in cloud machine-learning service platforms, when sensitive data are exposed to service providers. While private computing environments (e.g., secure enclaves), and cryptographic approaches (e.g.,…
Data Distribution Service (DDS) is an innovative approach towards communication in ICS/IoT infrastructure and robotics. Being based on the cross-platform and cross-language API to be applicable in any computerised device, it offers the…
Current low-latency anonymity systems use complex overlay networks to conceal a user's IP address, introducing significant latency and network efficiency penalties compared to normal Internet usage. Rather than obfuscating network identity…
Differential privacy (DP) is a privacy-enhancement technology (PET) that receives prominent attention from the academia, industry, and government. One main development over the past decade has been the decentralization of DP, including…
Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT), which significantly promotes the development of Industrial 4.0. However,…