Related papers: A Survey on Privacy-Preserving Caching at Network …
Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy…
Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development. The content generated by related applications, such as text, images and audio, has sparked a heated discussion. Various derived AIGC…
The ever-growing advances of deep learning in many areas including vision, recommendation systems, natural language processing, etc., have led to the adoption of Deep Neural Networks (DNNs) in production systems. The availability of large…
Network latency in mobile software has a large impact on user experience, with potentially severe economic consequences. Prefetching and caching have been shown effective in reducing the latencies in browser-based systems. However, those…
Millions of users across the world leverages data processing and sharing benefits from cloud environment. Data security and privacy are inevitable requirement of cloud environment. Massive usage and sharing of data among users opens door to…
Edge computing promises to reshape the centralized nature of today's cloud-based applications by bringing computing resources, at least in part, closer to the user. Reasons include the increasing need for real-time (short-delay,…
Caching in multi-cell networks faces a well-known dilemma, i.e., to cache same contents among multiple edge nodes (ENs) to enable transmission cooperation/diversity for higher transmission efficiency, or to cache different contents to…
Replicating or caching popular content in memories distributed across the network is a technique to reduce peak network loads. Conventionally, the performance gain of caching was thought to result from making part of the requested data…
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving…
Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and…
In order to reduce the latency of data delivery, one of techniques is to cache the popular contents at the base stations (BSs) i.e. edge caching. However, the technique of caching at edge can only reduce the backhaul delay, other techniques…
Optimal caching of files in a content distribution network (CDN) is a problem of fundamental and growing commercial interest. Although many different caching algorithms are in use today, the fundamental performance limits of network caching…
The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training dataset contains sensitive content, e.g., facial or medical…
Content storage at the network edge is a promising solution to mitigate the excessive traffic load due to on-demand streaming applications as well as to reduce the streaming delay. To this end, cache-enabled cellular architectures can be…
How do we interpret the differential privacy (DP) guarantee for network data? We take a deep dive into a popular form of network DP ($\varepsilon$--edge DP) to find that many of its common interpretations are flawed. Drawing on prior work…
Offloading high-demanding applications to the edge provides better quality of experience (QoE) for users with limited hardware devices. However, to maintain a competitive QoE, infrastructure, and service providers must adapt to users'…
The importance of intelligent data placement, management, and analysis has become apparent as scientific data volumes across the network continue to increase. To that end, we describe the use of in-network caching service deployments as a…
Caching popular contents at edge devices is an effective solution to alleviate the burden of the backhaul networks. Earlier investigations commonly neglected the storage cost in caching. More recently, retention-aware caching, where both…
The advent of the Edge Computing (EC) leads to a huge ecosystem where numerous nodes can interact with data collection devices located close to end users. Human detection and tracking can be realized at edge nodes that perform the…
With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that…