PROTECTING LOCATION PRIVACY FOR TASK ALLOCATION

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SPECIAL ISSUE - 5^thInter National Level Conference - “MEEMIC – 2019”

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PROTECTING LOCATION PRIVACY FOR TASK ALLOCATION

U.Pavanya, M.Ramya, N.Surya, Guided by

Dr.A.V.SanthoshBabu, Assistant Professor, Department Of Information Technology,

Sengunthar College of Engineering, Tiruchengode, Tamilnadu, India.

Manuscript History

Number: IJIRIS/RS/Vol.06/Issue03/MRBIS10084 DOI: 10.26562/IJIRAE.2019.MRIS10084

Received: 03, March 2019 Final Correction: 11, March 2019 Final Accepted: 18 Marcy 2019 Published: March 2019

Editor: Dr.A.Arul L.S, Chief Editor, IJIRIS, AM Publications, India

Copyright: ©2019 This is an open access article distributed under the terms of the Creative Commons Attribution License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Abstract - 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.

I. INTRODUCTION

Nowadays, 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 (Global Positioning System), accelerometer, gyroscope, and compass. The data (e.g., position, speed, temperature, heart rate) generated by these sensors enable many useful mobile applications, including location-based services mobile sensing, and mobile crowd sourcing. 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”).

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The rich cloud resources in cloud computing can be exploited to increase, enhance, and optimize capabilities of mobile devices, leading to the concept of MCC. According to [6], 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.

II. LITERATURE SURVEY:

The Mobile Sensing Platform: An Embedded Activity Recognition System

Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.

Location-based crowd sourcing: Extending crowd sourcing to the real world

The WWW and the mobile phone have become an essential means for sharing implicitly and explicitly generated information and a communication platform for many people. With the increasing ubiquity of location sensing included in mobile devices we investigate the arising opportunities for mobile crowd sourcing making use of the real world context. In this paper we assess how the idea of user-generated content, web-based crowd sourcing, and mobile electronic coordination can be combined to extend crowd sourcing beyond the digital domain and link it to tasks in the real world. To explore our concept we implemented a crowd-sourcing platform that integrates location as a parameter for distributing tasks to workers. In the paper we describe the concept and design of the platform and discuss the results of two user studies. Overall the findings show that integrating tasks in the physical world is useful and feasible. We observed that (1) mobile workers prefer to pull tasks rather than getting them pushed, (2) requests for pictures were the most favored tasks, and (3) users tended to solve tasks mainly in close proximity to their homes. Based on this, we discuss issues that should be considered during designing mobile crowd sourcing applications.

A Survey of Mobile Cloud Computing Application Models

Smart phones are now capable of supporting a wide range of applications, many of which demand an ever increasing computational power. This poses a challenge because smart phones are resource-constrained devices with limited computation power, memory, storage, and energy. Fortunately, the cloud computing technology offers virtually unlimited dynamic resources for computation, storage, and service provision. Therefore, researchers envision extending cloud computing services to mobile devices to overcome the smart phones constraints. The challenge in doing so is that the traditional smart phone application models do not support the development of applications that can incorporate cloud computing features and requires specialized mobile cloud application models. This article presents mobile cloud architecture, offloading decision affecting entities, application models classification, the latest mobile cloud application models, their critical analysis and future research directions.

Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges

Recently, Cloud-based Mobile Augmentation (CMA) approaches have gained remarkable ground from academia and industry. CMA is the state-of-the-art mobile augmentation model that employs resource-rich clouds to increase, enhance, and optimize computing capabilities of mobile devices aiming at execution of resource-intensive mobile applications. Augmented mobile devices envision to perform extensive computations and to store big data beyond their intrinsic capabilities with least footprint and vulnerability. Researchers utilize varied cloud-based computing resources (e.g., distant clouds and nearby mobile nodes) to meet various computing requirements of mobile users.

However, employing cloud-based computing resources is not a straightforward panacea. Comprehending critical factors (e.g., current state of mobile client and remote resources) that impact on augmentation process and optimum selection of cloud-based resource types are some challenges that hinder CMA adaptability. This paper comprehensively surveys the mobile augmentation domain and presents taxonomy of CMA approaches. The objectives of this study is to highlight the effects of remote resources on the quality and reliability of augmentation processes and discuss the challenges and opportunities of employing varied cloud-based resources in augmenting mobile devices. We present augmentation definition, motivation, and taxonomy of augmentation types, including traditional and cloud-based. We critically analyze the state-of-the-art CMA approaches and classify them into four groups of distant fixed, proximate fixed, proximate mobile, and hybrid to present taxonomy.

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© 2014- 19, IJIRIS- All Rights Reserved Page -210 Vital decision making and performance limitation factors that influence on the adoption of CMA approaches are introduced and an exemplary decision making flowchart for future CMA approaches are presented. Impacts of CMA approaches on mobile computing is discussed and open challenges are presented as the future research directions.

Dynamic Mobile Cloud Computing: Ad Hoc and Opportunistic Job Sharing

Despite increasing usage of mobile computing, exploiting its full potential is difficult due to problems such as resource sparseness. In this paper, we explore the feasibility of a mobile cloud computing framework to use local resources to solve these problems. The framework aims to determine a priori the usefulness of sharing workload at runtime. The results of experiments conducted in Bluetooth transmission and an initial prototype are also presented. Furthermore, we discuss a preliminary analytical model to determine whether or not a speedup will be possible in offloading.

A virtual cloud computing provider for mobile devices

Along with the explosive growth of the mobile applications and up-raising cloud computing concept, mobile cloud computing appears to be a new potential technology for mobile services. A mobile cloud computing map cloud computing ideas on top of the mobile environment, and overcomes obstacles that deal with performance (i.e.

battery life, CPU, storage, and bandwidth), environment (i.e. heterogeneity, scalability, availability) and security (i.e.

reliability, privacy) discussed in mobile computing. In this position paper we are going to focus in depth on a Hadoop based framework for ad-hoc mobile cloud computing - we specifically refer to a research paper and the authors' decision to use and port Hadoop to build a Virtual Cloud Computing Provider for Mobile Devices. In the beginning, we are going to outline a short introduction and enumerate the challenges that we face implementing a framework for mobile cloud computing. We conclude this paper with a short example of other Map-Reduce based MCC framework, which achieved better performance using its own custom implementation.

III. EXISTING SYSTEM

In order to allocate tasks and provide effective services, mobile servers in an ad hoc mobile cloud need to share their location data with the CCP, which could reveal a lot of personal information such as a user’s identity, health status, personal activities, and political views. Hence, it is mandatory to provide privacy guarantee in order to engage more mobile devices in the cloud. Finally, there is an inherent conflict between quality of service (i.e., utility) and privacy in task allocation. If an ad hoc mobile cloud ensures privacy of mobile servers, it is difficult to guarantee the utility of their MCC service. Finding a solution that ensures privacy while guaranteeing utility for task allocation is a major challenge in such systems.

3.1 DRAWBACK

Despite many promising applications, ad hoc mobile clouds pose several challenges. First, mobile cloud resources in an ad hoc mobile cloud are dynamic and diverse. As a result, some mobile servers may drop the task they are performing and leave the cloud. Some mobile servers may be “spammers” that only want to collect rewards and submit arbitrary answers without looking at the specific task. Moreover, some mobile servers may not be powerful enough to provide sensing data at the required accuracy. Therefore, how to allocate tasks to ensure the quality of the service provided by these dynamic mobile servers is challenging. Second, as pointed out by, security and privacy of mobile devices as service providers is a critical concern in the ad hoc mobile cloud.

3.2 PROPOSED SYSTEM

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 construct a new structure called Reputation- based 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.

IV. ADVANTAGES

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. 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. We use a geo cast 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. We conduct extensive experiments based on real-world datasets to show the effectiveness of the proposed framework. Multimedia data delivery in wireless mesh network”

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All projects are feasible when given unlimited resources and infinite time. It is both necessary and prudent to evaluate the feasibility of a project at the earliest possible time.

A feasibility study is not warranted for systems in which economic justification is obvious, technical risk is low, few legal problems are expected and no reasonable alternative exists. An estimate is made of whether the identified user needs may be satisfied using current software and hardware technologies. The study will decide if the proposed system will be cost effective from the business point of view and if it can be developed in the given existing budgetary constraints. The feasibility study should be relatively cheap and quick. The result should inform the decision of whether to go ahead with a more detailed analysis.

The Java Programming Language

With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform- independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works. You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.

The Java Platform

A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.

The Java platform has two components:

 The Java Virtual Machine (Java VM)

 The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

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© 2014- 19, IJIRIS- All Rights Reserved Page -212 The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide. The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware. Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform- independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well- tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.

VI. NETWORKING TCP/IP stack

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s

The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.

UDP

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers.

TCP

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address. This encodes a network ID and more addressing. The network ID falls into various classes according to the size of the network address.

Network address

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10- bit addressing, allowing 1024 different hosts.

Host address

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address

The 32 bit address is usually written as 4 integers separated by dots.

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A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are "well known".

Private Spatial Decomposition (PSD):

The Private Spatial Decomposition (PSD) approach is first introduced in [18] to construct a spatial dataset that achieves DP. A PSD is a spatial index where each index node is associated with a spatial region, and the value for each node is the noisy count of data points (mobile servers in our scenario) in that region. The data structure for spatial index can be grids, k-d trees, or quad trees.

K-d trees:

On the other hand, object-based structures such as k-d trees split space based on the locations of mobile servers.

Since location data are used both for calculating splitting positions and computing noisy counts, the privacy budget should be split between the two processes as well. Object-based structures are expected to be more balanced than space-based PSD; however, they are not very robust in the sense that their accuracy may decrease abruptly with a slight change of the PSD parameters or input dataset distributions.

Adaptive grid (AG):

The work proposes an adaptive grid (AG) approach with two-level grids. The first-level grid is uniformly divided, and the granularity of the second-level grid depends on the noisy counts obtained in the first-level. AG is a hybrid approach that inherits the simplicity and robustness of space-based approach, but still utilizes some data-dependent information when choosing the granularity for the second-level grid. In this paper, we adapt their approach to construct our PSD.

Mobile Server Characteristics

Tasks considered in the system are location-dependent, i.e., they must be performed at specific locations. Typical examples include sensing tasks and those in location-based services. In many cases, the mobile server needs to travel physically to the location associated with the task. Therefore, most mobile servers that perform a task will be located in close proximity to the task location. Furthermore, it is not uncommon that some tasks need to be performed by more than one mobile server.

Performance Metrics

This section presents a task allocation model that effectively allocates tasks among mobile servers in the MCC system while providing differential location privacy for mobile servers. Adding privacy protection to task allocation greatly complicates the problem since the CCP can no longer allocate a task among mobile servers based on their exact locations. Due to the uncertain nature of DP, it is possible that there is no mobile server in a geocast region, even if the noisy count shows positive. Thus the task may not be completed as no, or an insufficient number of mobile servers are actually notified. Also, if the task is allocated to mostly mobile servers with low reputation scores, the result may not satisfy the quality-of-service requirement for the task.

VII. CONCLUSION

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 real world datasets to demonstrate the effectiveness of our proposed framework.

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The need for efficient methods of Emergency call has become a necessity for the safety of every individual in this society since there exist a lot of illegal acts. Better methods of emergency call can protect those people who use the android devices and this can reduce the trapping of human being in various situations and places.

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