Direct User Data Authentication in Cloud

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1 International Journal of Electronics and Computer Science Engineering 1954 Available Online at ISSN Direct User Data Authentication in Cloud P. N. karthikayan 1, C. B. Selvalakshmi 2, Mallikarjuna Nandi 3 (Department of Computer Science & Engg, Velammal college of Engineering and Technology, Maduari) 1, 3 Abstract- Cloud computing is the long dreamed vision of computing as a utility, where data owners can remotely store their data in the cloud to enjoy on-demand high-quality applications and services from a shared pool of configurable computing resources. While data outsourcing relieves the owners of the burden of local data storage and maintenance, it also eliminates their physical control of storage dependability and security, which traditionally has been expected by both enterprises and individuals with high service-level requirements. In order to facilitate rapid deployment of cloud data storage service and regain security assurances with outsourced data dependability, efficient methods that enable ondemand data correctness verification on behalf of cloud data owners have to be designed. In this article we propose that publicly auditable cloud data storage is able to help this nascent cloud economy become fully established. With public audit ability, a trusted entity with expertise and capabilities data owners do not possess can be delegated as an external audit party to assess the risk of outsourced data when needed. Such an auditing service not only helps save data owners computation resources but also provides a transparent yet cost-effective method for data owners to gain trust in the cloud. We describe approaches and system requirements that should be brought into consideration, and outline challenges that need to be resolved for such a publicly auditable secure cloud storage service to become a reality. Keywords Data Authenticating, Third party audit, Ubiquitous computing 1. CLOUD COMPUTING I. INTRODUCTION Cloud computing has been envisioned as the next generation architecture of the IT enterprise due to its long list of unprecedented advantages in IT: on demand self-service, ubiquitous network access, location-independent resource pooling, rapid resource elasticity, usage-based pricing, and transference of risk. One fundamental aspect of this new computing model is that data is being centralized or outsourced into the cloud. From the data owners perspective, including both individuals and IT enterprises, storing data remotely in a cloud in a flexible on-demand manner brings appealing benefits: relief of the burden of storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, personnel maintenance, and so on. While cloud computing makes these advantages more appealing than ever, it also brings new and challenging security threats to the outsourced data. Since cloud service providers (CSP) are separate administrative entities, data outsourcing actually relinquishes the owner s ultimate control over the fate of their data. As a result, the correctness of the data in the cloud is put at risk due to the following reasons. First of all, although the infrastructures under the cloud are much more powerful and reliable than personal computing devices, they still face a broad range of both internal and external threats to data integrity. Outages and security breaches of noteworthy cloud services appear from time to time. Amazon S3 s recent downtime, Gmail s mass deletion incident, and Apple mobile ME s post-launch downtime are all such examples. Second, for benefits of their own, there are various motivations for CSPs to behave unfaithfully toward cloud customers regarding the status of their outsourced data. Examples include CSPs, for monetary reasons, reclaiming storage by discarding data that has not been or is rarely accessed or even hiding data loss incidents to maintain a reputation In short, although outsourcing data into the cloud is economically attractive for the cost and complexity of long-term large-scale data storage, it does not offer any guarantee on data integrity and availability. This problem, if not properly addressed, may impede successful deployment of the cloud architecture. 1.1 CLOUD COMPUTING SERVICES Cloud computing providers offer their services according to three fundamental models: Infrastructure As A Service (IAAS), Platform As A Service (PAAS), and Software As A Service (SAAS) where IAAS is the most basic and each higher model abstracts from the details of the lower models. ISSN /V1N

2 Direct User Data Authentication in Cloud 1955 Infrastructure as a Service (Iaas) Platform as a Service(Paas) Software as a Service(Saas) Cloud computing is a general term for anything that involves delivering hosted services over the Internet. These services are broadly divided into three categories: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). The name cloud computing was inspired by the cloud symbol that's often used to represent the Internet in flowcharts and diagrams. A cloud service has three distinct characteristics that differentiate it from traditional hosting. It is sold on demand, typically by the minute or the hour; it is elastic -- a user can have as much or as little of a service as they want at any given time; and the service is fully managed by the provider (the consumer needs nothing but a personal computer and Internet access). Significant innovations in virtualization and distributed computing, as well as improved access to high-speed Internet and a weak economy, have accelerated interest in cloud computing. A cloud can be private or public. A public cloud sells services to anyone on the Internet. (Currently, Amazon Web Services is the largest public cloud provider.) A private cloud is a proprietary network or a data center that supplies hosted services to a limited number of people. When a service provider uses public cloud resources to create their private cloud, the result is called a virtual private cloud. Private or public, the goal of cloud computing is to provide easy, scalable access to computing resources and IT services. Infrastructure-as-a-Service like Amazon Web Services provides virtual server instance API) to start, stop, access and configure their virtual servers and storage. In the enterprise, cloud computing allows a company to pay for only as much capacity as is needed, and bring more online as soon as required. Because this pay-for-what-you-use model resembles the way electricity, fuel and water are consumed, it's sometimes referred to as utility computing. Platform-as-a-service in the cloud is defined as a set of software and product development tools hosted on the provider's infrastructure. Developers create applications on the provider's platform over the Internet. PaaS providers may use APIs, website portals or gateway software installed on the customer's computer. Force.com, (an outgrowth of Salesforce.com) and Google Apps are examples of PaaS. Developers need to know that currently, there are not standards for interoperability or data portability in the cloud. Some providers will not allow software created by their customers to be moved off the provider's platform. In the software-as-a-service cloud model, the vendor supplies the hardware infrastructure, the software product and interacts with the user through a front-end portal. SaaS is a very broad market. Services can be anything from Webbased to inventory control and database processing. Because the service provider hosts both the application and the data, the end user is free to use the service from anywhere. PRIVATE CLOUD Private cloud (also called internal cloud or corporate cloud) is a marketing term for a proprietary computing architecture that provides hosted services to a limited number of people behind a firewall.advances in virtualization and distributed computing have allowed corporate network and datacenter administrators to effectively become service providers that meet the needs of their "customers" within the corporation. Marketing media that uses the words "private cloud" is designed to appeal to an organization that needs or wants more control over their data than they can get by using a third-party hosted service such as Amazon's Elastic Compute Cloud (EC2) or Simple Storage Service (S3). The goal of constructing a private cloud can be acquiring IaaS, PaaS, or SaaS. Based on the objectives, an organization, for example, may simply seek the ability to efficiently deploy/manage servers to provide maximal flexibility for developing and testing applications, and in this case IaaS is what and all the organization needs.

3 IJECSE,Volume1,Number 4 P. N. karthikayan et al INFORMATION SECURITY THREATS & VULNERABILITIES E-Government security requirements can be studied by examining the overall process, beginning with the consumer and ending with the e-gov Server. The asset that must be protected to ensure e-gov include client computers, the messages travelling on the communication channel, and the and the Web and e-gov servers including any hardware attached to the servers. Client Threats: Until the introduction of executable Web content, Web pages were mainly static. Confidentiality Threats: Confidentiality is the prevention of unauthorized information disclosure. Use of Internet definitely poses confidentiality threats to the messages sent. Malicious Codes: Computer viruses, worms and Trojan Horses are examples of malicious code. People are aware but may not be prepared to deal with such adversaries. Integrity Threats: An integrity threat exists when an unauthorized party can alter a message stream of information. Unauthorized changes and defacing of web-pages may put any e-gov project into jeopardy. Availability Threats: The purpose of availability threats, also known as delay or denial of service threats, is to disrupt normal computer processing or to deny processing entirely. Slowing any Internet service will detract citizens from using egov services. 1.3 INFORMATION SECURITY REQUIREMENTS These needs are governed by the necessity to protect the following security attributes: Authentication: This is the ability to say that an electronic communication (whether via or web) does genuinely come from who it purports to. Forging the "From" field in an header is a trivial matter, and far more sophisticated attacks are standard fare for hackers. The traditional method of using passwords alone is no more secure. Privacy: Privacy is the ability to ensure that information is accessed and changed only by authorized parties. Typically this is achieved by enforcing strong security controls in the server systems and via encryption. Authorization: Authorization allows a person or computer system to determine if someone has the authority to request or approve an action or information. Authorization is tied with Authentication. Integrity: The Integrity of information means ensuring that a communication received has not been altered or tampered with.

4 Direct User Data Authentication in Cloud 1957 Trust: Trust is one of the most vital factors that determine the quality of a cloud organization. There must be a mutual understanding and complete trust between the cloud user and the cloud provider while accessing the cloud data. II. PROBLEM DEFINATION Data outsourcing to cloud storage servers is raising trend among many firms and users owing to its economic advantages. This essentially means that the owner (client) of the data moves its data to a third party cloud storage server which is supposed to - presumably for a fee - faithfully store the data with it and provide it back to the owner whenever required. As data generation is far outpacing data storage it proves costly for small firms to frequently update their hardware whenever additional data is created. Also maintaining the storages can be a difficult task. Storage outsourcing of data to a cloud storage helps such firms by reducing the costs of storage, maintenance and personnel. It can also assure a reliable storage of important data by keeping multiple copies of the data thereby reducing the chance of losing data by hardware failures. Storing of user data in the cloud despite its advantages has many interesting security concerns which need to be extensively investigated for making it a reliable solution to the problem of avoiding local storage of data. Many problems like data authentication and integrity (i.e., how to efficiently and securely ensure that the cloud storage server returns correct and complete results in response to its clients queries [1]), outsourcing encrypted data and associated difficult problems dealing with querying over encrypted domain [2] were discussed in research literature. In this paper we deal with the problem of implementing a protocol for obtaining a proof of data possession in the cloud sometimes referred to as Proof of retrievability (POR).This problem tries to obtain and verify a proof that the data that is stored by a user at a remote data storage in the cloud (called cloud storage archives or simply archives) is not modified by the archive and thereby the integrity of the data is assured. Such kinds of proofs are very much helpful in peer-to-peer storage systems, network file systems, long term archives, web-service object stores, and database systems. Such verification systems prevent the cloud storage archives from misrepresenting or modifying the data stored at it without the consent of the data owner by using frequent checks on the storage archives. Such checks must allow the data owner to efficiently, frequently, quickly and securely verify that the cloud archive is not cheating the owner. Cheating, in this context, means that the storage archive might delete some of the data or may modify some of the data. It must be noted that the storage server might not be malicious; instead, it might be simply unreliable and lose or inadvertently corrupt the hosted data. But the data integrity schemes that are to be developed need to be equally applicable for malicious as well as unreliable cloud storage servers. Any such proofs of data possession schemes do not, by itself, protect the data from corruption by the archive. It just allows detection of tampering or deletion of a remotely located file at an unreliable cloud storage server. To ensure file robustness other kind of techniques like data redundancy across multiple systems can be maintained.

5 IJECSE,Volume1,Number 4 P. N. karthikayan et al OUR CONTRIBUTION We present a scheme which does not involve the encryption of the whole data. We encrypt only few bits of data per data block thus reducing the computational overhead on the clients. Fig. 2. A data file F with 6 data blocks The client storage overhead is also minimized as it does not store any data with it. Hence our scheme suits well for thin clients. In this paper we have worked to facilitate the client in getting a proof of integrity of the data which he wishes to store in the cloud storage servers with bare minimum costs and efforts. Our scheme was developed to reduce the computational and storage overhead of the client as well as to minimize the computational overhead of the cloud storage server. We also minimized the size of the proof of data integrity so as to reduce the network bandwidth consumption. Current data processing frameworks like Google s Map Reduce or Microsoft s Dryad engine have been designed for cluster environments. This is reflected in a number of assumptions they make which are not necessarily valid in cloud environments. In this section we discuss how abandoning these assumptions raises new opportunities but also challenges for efficient parallel data processing in clouds. Storage and computational process. network bandwidth consumption Parallel Data Processing in the Cloud performance degrade and high bandwidth consumption That is higher than the actual cost. Setting the right price for a service is a non-trivial problem, because when there is competition the demand for services grows inversely but not proportionally to the price. There are two major challenges when trying to define an optimal pricing scheme for the cloud caching service. The first is to define a simplified enough model of the price demand dependency, to achieve a feasible pricing solution, but not oversimplified model that is not representative. For example, a static pricing scheme cannot be optimal if the demand for services has deterministic seasonal fluctuations. The cloud caching service can maximize its profit using an optimal pricing scheme. This work proposes a pricing scheme along the insight that it is sufficient to use a simplified price-demand model which can be re-evaluated in order to adapt to model mismatches, external disturbances and errors, employing feedback from the real system behavior and performing refinement of the optimization procedure. Overall, optimal pricing necessitates an appropriately simplified price-demand model that incorporates the correlations of structures in the cache services. The pricing scheme should be adaptable to time changes.simple but not simplistic price-demand modeling. We model the price-demand dependency employing second order differential equations with constant parameters. This modeling is flexible enough to represent a wide variety of demands as a function of price. The simplification of using constant parameters allows their easy estimation based on given price-demand data sets. The model takes into account that structures can be available in the cache or can be discarded if there is not enough respective demand. Optional structure availability allows for optimal scheduling

6 Direct User Data Authentication in Cloud 1959 of structure availability, such that the cloud profit is maximized.the model of price-demand dependency for a set of structures incorporates their correlation in query execution. CONCLUSION AND FUTURE WORKS: The data can be directly accessed in the database by the user successfully The network bandwidth is also minimized as the size of the proof is comparatively very less(k+1 bits for one proof). It should be noted that our scheme applies only to static storage of data. It cannot handle to case when the data need to be dynamically changed. Hence developing on this will be a future challenge. Also the number of queries that can be asked by the client is fixed apriori. But this number is quite large and can be sufficient if the period of data storage is short. It will be a challenge to increase the number of queries using this scheme. REFERENCES [1] E. Mykletun, M. Narasimha, and G. Tsudik, Authentication and integrity in outsourced databases, Trans. Storage, vol. 2, no. 2, pp , [2] D. X. Song, D. Wagner, and A. Perrig, Practical techniques for searches on encrypted data, published in Security and Privacy, Proceedings at IEEE Symposium., Page(s): 44-55, Publication Year: [3]Socie Y. Zhao, M. Hategan, B. Clifford, I. Foster, G. von Laszewski, V. Nefedova, I. Raicu, T. Stef-Praun, and M. Wilde. Swift: Fast, Reliable, Loosely Coupled Parallel Computation. In Services, 2007 IEEE Congress on, pages , July [4] I. Foster and C. Kesselman. Globus: A Metacomputing Infrastructure Toolkit. Intl. Journal of Supercomputer Applications, 11(2): , [5] J. Frey, T. Tannenbaum, M. Livny, I. Foster, and S. Tuecke. Condor- G: A Computation Management Agent for Multi- Institutional Grids. Cluster Computing, 5(3): , 2002.

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