iosr-JCE   Volume-12 ~ Issue-4

Abstract: Cloud computing has been envisioned as next-generation architecture of IT enterprise. Cloud computing is the internet based model which delivers computing services on demand. Cloud storage moves the users data to large data centers, which are remotely located and on which user does not have any control. Apart having more benefits, this unique feature of the cloud also poses many new security challenges which need to be clearly understood and resolved. The important concerns that need to be addressed in cloud computing is to assure the integrity of data i.e. correctness of his data in the cloud. In this paper we provide a scheme which gives a proof of data integrity in the cloud which the customer can employ to check the correctness of his data in the cloud. This proof can be done by both the cloud and the customer and can be incorporated in the Service level agreement (SLA). Here we are encrypting the hash values and providing the more security. This scheme ensures that the storage at the client side is minimal which will be beneficial for thin clients. Here the computational time is minimized by using integrity and encryption methodology on the data in cloud . It should be noted that this scheme applies only to archive storage data.

Key words : Cloud Computing, Data Integrity, Data Encryption.

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[2] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, "Provable data possession at untrusted stores," in CCS ‟07: Proceedings of the 14th ACM conference on Computer and communications security. New York, NY, USA: ACM, 2007, pp. 598–609.
[3] Juels and B. S. Kaliski, Jr., "Pors: proofs of retrievability for large files," in CCS ‟07: Proceedings of the 14th ACM conference on Computer and communications security. New York, NY, USA: ACM, 2007, pp.584–597.
[4] Sravan Kumar R, Ashutosh Saxena, "Data Integrity Proofs in Cloud Storage" 978-1-4244-8953-4/11/$26.00 © 2011 IEEE.
[5] Ku. Swati G. Anantwar, Prof. Karuna G. Bagde, "Data Integrity and Security in Cloud," International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 1, Issue 7, September 2012

Abstract:This Article Presents The Design Of Continuous Video Streaming Compression Of A Networked System For Joint Compression, Error Detection And For Error Correction. Video Compression Applications Are Becoming More Popular Over Wireless Multimedia Sensor Networks. To Save Energy In Bit Transmissions And Receptions Over A Video Sensor Network, The Video File Contents Need To Be Encoded Packet By Packet Before Its Transmission Over Network. The Quality At The Receiver Side Can Be Maximized By Designing The Cross Layer System. The Transmission Rate And Encoding Rate Can Be Controlled By The Cross Layer System. The Fairness Of The Received Video Quality Can Be Developed By The Rate Controller. Instead, An Adaptive Parity Scheme That Drops Samples In Error Is Proposed And Shown To Improve Video Quality. Finally A Low Complexity, Adaptive Video Encoder, Is Proposed That Performs Low Complexity Motion Estimation On Sensors And Implemented To Further Optimize Energy Consumption., Thus Greatly Reducing The Amount Of Data To Be Transmitted And Video File Can Be Decompressed.

Index Terms: Compression, Multimedia Streaming, Congestion Control, Sensor Networks, Rate Controller.

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[2] I. F. Akyildiz, T. Melodia, And K. R. Chowdhury, ―A Survey On Wireless Multimedia Sensor Networks,‖ Computer Networks (Elsevier), Vol. 51, No. 4, Pp. 921–960, Mar. 2007.

[3] A. Aaron, S. Rane, R. Zhang, And B. Girod, ―Wyner-Ziv Coding For Video: Applications To Compression And Error Resilience,‖ In Proc. Of IEEE Data Compression Conf. (DCC), Snowbird, UT, March 2003, Pp. 93–102.

[4] D. Donoho, ―Compressed Sensing,‖ IEEE Transactions On Information Theory, Vol. 52, No. 4, Pp. 1289–1306, Apr. 2006.

[5] B. Girod, A. Aaron, S. Rane, And D. Rebollo-Monedero, ―Distributed Video Coding,‖ Proc. Of The IEEE, Vol. 93, No. 1, Pp. 71–83, January 2005.

[6] T. Sheng, G. Hua, H. Guo, J. Zhou, And C. W. Chen, ―Rate Allocation For Transform Domain Wyner-Ziv Video Coding Without Feedback,‖ In ACM Intl. Conf. On Multimedia, New York, NY, USA, October 2008, Pp. 701–704.

[7] J. Romberg,‖Imaging Via Compressive Sampling,‖ IEEE Signal Processing Mag Azine,Vol.25,No.2,Pp.14-20,2008

[8] M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, And R. Baraniuk, ―Single-Pixel Imaging Via Compressive Sampling,‖ IEEE Signal Processing Magazine, Vol. 25, No. 2, Pp. 83—91, 2008.

[9] M . Wakin, J. Laska, M. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. Kelly, And R. Baraniuk, ―Compressive Imaging For Video Representation And Co

Abstract: Unstructured peer-to-peer file sharing networks are very popular in the market. They introduce network traffic. The resultant networks are not perform search efficiently because existing overlay topology formation algorithms are creates unstructured P2P networks without performance guarantees. In this paper, We compares structured and unstructured overlays, Showing through analytical and simulation results how an unstructured solution relying on a scale-free topology is an effective option to deploy for offering services based on equivalent servants. Based on this result, The EQUivalent servAnt locaTOR (EQUATOR) architecture, which overcomes the issues related to the deployment of a scale-free topology for service location in a real network, mainly due to the static nature of the ideal scale-free construction algorithm and the lack of a global knowledge of the participating peers. Simulation results confirmed the effectiveness of EQUATOR, showing how it offers good lookup performance in combination with low message overhead and high resiliency to node churn and failures. Some possible future works are introduced and are related to some complementary issues ranging from the proximity-aware selection of servants to the introduction of proper incentives to encourage nodes to join the EQUATOR overlay and offer their resources.

Keywords: peer to peer systems, Random Networks, Scale-Free Networks, Communication Networks, Socio-Technical Networks, Diffusion.

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