714 research outputs found
A Cloud-based Mobile Privacy Protection System with Efficient Cache Mechanism
People increasingly rely on their mobile devices and use them to store a lot of data. Some of the data are personal and private, whose leakage leads to users\u27 privacy harm. Meanwhile, mobile apps and services over-collect users\u27 data due to the coarse-grained access control approach utilized by the mobile operating system. We propose a cloud-based approach to provide fine-grained access control toward data requests. We add privacy level, as a new metadata, to data and manage the storage using different policies correspondingly. However, the proposed approach leads to performance decreases because of the extra communication cost. We also introduce a novel cache mechanism to eliminate the extra cost by storing non-private and popular data on the mobile device. As part of our cache mechanism, we design a user-preference-based ordering method along with the principle of locality to determine how popular some data are. We also design a configurable refresh policy to improve the overall performance. Finally, we evaluate our approach using a real phone in a simulated environment. The results show that our approach can keep the response time of all data requests within a reasonable range and the cache mechanism can further improve the performance
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Artificial Intelligence-Generated Content (AIGC) is an automated method for
generating, manipulating, and modifying valuable and diverse data using AI
algorithms creatively. This survey paper focuses on the deployment of AIGC
applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile
AIGC networks, that provide personalized and customized AIGC services in real
time while maintaining user privacy. We begin by introducing the background and
fundamentals of generative models and the lifecycle of AIGC services at mobile
AIGC networks, which includes data collection, training, finetuning, inference,
and product management. We then discuss the collaborative cloud-edge-mobile
infrastructure and technologies required to support AIGC services and enable
users to access AIGC at mobile edge networks. Furthermore, we explore
AIGCdriven creative applications and use cases for mobile AIGC networks.
Additionally, we discuss the implementation, security, and privacy challenges
of deploying mobile AIGC networks. Finally, we highlight some future research
directions and open issues for the full realization of mobile AIGC networks
cRVR: A Stackelberg Game Approach for Joint Privacy-Aware Video Requesting and Edge Caching
As users conveniently stream their favored online videos, video request
records will be automatically seized by video content providers, which may leak
users' privacy. Unfortunately, most existing privacy-enhancing approaches are
not applicable for protecting users' privacy in requests, which cannot be
easily altered or distorted by users and must be visible for content providers
to stream correct videos. To preserve request privacy in online video services,
it is possible to request additional videos irrelevant to users' interests so
that content providers cannot precisely infer users' interest information.
However, a naive redundant requesting approach will significantly degrade the
performance of edge caches and increase bandwidth overhead accordingly. In this
paper, we are among the first to propose a Cache-Friendly Redundant Video
Requesting (cRVR) algorithm for User Devices (UDs) and its corresponding
caching algorithm for the Edge Cache (EC), which can effectively mitigate the
problem of request privacy leakage with minimal impact on the EC's performance.
To solve the problem, we develop a Stackelberg game to analyze the dedicated
interaction between UDs and EC and obtain their optimal strategies to maximize
their respective utility. For UDs, the utility function is a combination of
both video playback utility and privacy protection utility. We theoretically
prove the existence and uniqueness of the equilibrium of the Stackelberg game.
In the end, extensive experiments are conducted with real traces to demonstrate
that cRVR can effectively protect video request privacy by reducing up to
57.96\% of privacy disclosure compared to baseline algorithms. Meanwhile, the
caching performance of ECs is only slightly affected
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