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 geocast, 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. We investigate the capability of localizing node failures in communication networks from binary states (normal/failed) of end to end paths. Given a set of nodes of interest, uniquely localizing failures within this set requires that different observable path states associate with different node failure events. However, this condition is difficult to test on large networks due to the need to enumerate all possible node failures.


Now a days, mobile devices such as smartphones 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 crowdsourcing . Although improved largely over the past several years, mobile devices are still resource-constrained mainly due to the limited battery lifetime. 
One such approach, generally known as network tomography, focuses on inferring internal network characteristics based on end-to-end performance measurements from a subset of nodes with monitoring capabilities, referred to as monitors. Unlike direct measurement, network tomography only relies on end-to-end performance (e.g., path connectivity) experienced by data packets, thus addressing issues such as overhead, lack of protocol support, and silent failures. In cases where the network characteristic of interest is binary (e.g., normal or failed), this approach is known as Boolean network tomography. In this paper, we study an application of Boolean network tomography to localize node failures from measurements of path states.1 Under the assumption that a measurement path is normal if and only if all nodes on this path behave normally, we formulate the problem as a system of Boolean equations, where the unknown variables are the binary node states, and the known constants are the observed states of measurement paths. The goal of Boolean network tomography is essentially to solve this system of Boolean equations.
A recent work by To and Ghinita has been proposed to protect location privacy of crowdsourcing workers in spatial crowdsourcing. However, their solution does not consider worker reputation, and thus cannot provide any quality control over the final result. Therefore, it can not be easily applied to the mobile cloud computing scenario where service quality is very important.In this paper, we propose a framework that provides solutions to the above challenges, where both location privacy and service quality are considered. In our framework, the CCP only has access to sanitized location data of mobile servers according to differential privacy (DP). Since every mobile server is subscribed to a cellular service provider (CSP) with which it already has a trust relationship, the CSP can integrate mobile server location and reputation information, and provides the data to the CCP in noisy form according to DP. To generate the noisy mobile server data, we adapt the Private Spatial Decomposition (PSD) approach proposed in and construct a new structure called Reputationbased PSD (R-PSD). Since fake points need to be created in the DP model, geocast is used to disseminate tasks to mobile servers to prevent the CCP from identifying these points. To summarize, our main contributions are as follows: 1) We identify the specific challenges for task allocation in ad hoc mobile clouds, and propose a framework that can achieve differential privacy for mobile server location data while providing high service quality. 
2) We introduce a new structure called R-PSD that partitions the space based on both reputation and location information, and develop an efficient search strategy that finds appropriate R-PSD partitions to ensure high quality of service. 
3) We use a geocast mechanism when disseminating tasks to mobile servers to overcome the restrictions imposed by DP, and the overhead during this process is incorporated into the design of the search strategy. 
4) We conduct extensive experiments based on real-world datasets to show the effectiveness of the proposed framework.


System : Pentium IV 2.4 GHz.
Hard Disk        : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.


Operating system : Windows XP.
Coding Language :  JAVA
Data Base :  MYSQL


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. We studied the fundamental capability of a network in localizing failed nodes from binary measurements (normal/failed) of paths between monitors. We proposed two novel measures: maximum identifiability index that quantifies the scale of uniquely localizable failures writ a given node set, and maximum identifiable set that quantifies the scope of unique localization under a given scale of failures. We showed that both measures are functions of the maximum identifiability index per node. We studied these measures for three types of probing mechanisms that offer different controllability of probes and complexity of implementation. For each probing mechanism, we established necessary/sufficient conditions for unique failure localization based on network topology, placement of monitors, constraints on measurement paths, and scale of failures. We further showed that these conditions lead to tight upper/lower bounds on the maximum identifiability index, as well as inner/outer bounds on the maximum identifiable set. We showed that both the conditions and the bounds can be evaluated efficiently using polynomial time algorithms. Our evaluations on random and real network topologies showed that probing mechanisms that allow monitors to control the routing of probes have significantly better capability to uniquely localize failures.


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