PROTECTING LOCATION PRIVACY FOR TASK ALLOCATION IN AD HOC MOBILE CLOUD COMPUTING
Mobile cloud computing is an emerging cloud computing paradigm that integrates cloud computing and mobile computing to enable many useful mobile applications. However, the large-scale deployment of mobile cloud computing is hindered by the concerns on possible privacy leakage. In this paper, we investigate the privacy issues in the ad hoc mobile cloud computing, and propose a framework that can protect the location privacy when allocating tasks to mobile devices. Our mechanism is based on differential privacy and geo cast, and allows mobile devices to contribute their resources to the ad hoc mobile cloud without leaking their location information. We develop analytical models and task allocation strategies that balance privacy, utility, and system overhead in an ad hoc mobile cloud. We also conduct extensive experiments based on real-world datasets, and the results show that our framework can protect location privacy for mobile devices while providing effective services with low system overhead.
Index Terms—Mobile cloud computing, location privacy, task allocation, reputation.
mobile devices such as smart phones and tablets have gained tremendous popularity. These devices are often equipped with a variety of sensors such as camera, microphone, GPS, accelerometer, gyroscope, and compass. The data (e.g., position, speed, temperature, and heart rate) generated by these sensors enable many useful mobile applications, including location-based services, mobile sensing, and mobile crowd sourcing. Although improved largely over the past several years, mobile devices are still resource-constrained mainly due to the limited battery lifetime. On the other hand, cloud computing has widely been regarded as the next-generation computing paradigm which provides “unlimited” cloud resources to end-users in an on-demand fashion. The rich cloud resources in cloud computing can be exploited to increase, enhance, and optimize capabilities of mobile devices, leading to the concept of mobile cloud computing (MCC). According to , MCC integrates cloud computing technologies with mobile devices to make the mobile devices more capable in terms of computational power, memory, storage, energy, and context awareness. There are generally two types of mobile clouds in MCC: infrastructure-based and ad hoc. The infrastructure-based mobile cloud consists of stationary computing resources and provides services to the mobile users via the Internet. Alternatively, in the ad hoc mobile cloud, a collection of mobile devices (hereafter referred to as “mobile servers”) performs as cloud resources and provides access to local or Internet-based cloud services to other mobile users (hereafter referred to as “mobile clients”).
There are various types of MCC applications with varying system models. In this paper, we consider an emerging MCC system model favored by several recent studies. A unique feature of this system model is that various mobile devices such as smart phones, tablets, and handheld computing devices play the role of servers based on cloud computing principles. Benefits of this system include the proximity of the mobile resources to mobile clients and context-awareness of the mobile resources. In the following, we present the system model, describe the mobile server characteristics in Section, outline our threat model and assumptions in Section and discuss our performance metrics.
In this paper, we have investigated the privacy issues in the ad hoc mobile cloud computing, and have proposed a framework that protects the location privacy of mobile servers when allocating mobile cloud computing tasks. Considering the dynamic and diverse nature of mobile servers, we have designed a new data structure R-PSD and developed an efficient search strategy that finds appropriate R-PSD partitions to ensure high service quality. We have conducted extensive experiments based on realworld datasets to demonstrate the effectiveness of our proposed framework.
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