AN EFFICIENT AUDIT SERVICE OUTSOURCING FOR DATA IN TEGRITY IN CLOUDS

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1 AN EFFICIENT AUDIT SERVICE OUTSOURCING FOR DATA IN TEGRITY IN CLOUDS Mrs.K.Saranya, M.E.,(CSE), Jay Shriram Group of Institutions, Tirupur. Dr.S.Rajalakshmi, Associate Professor/CSE, Jay Shriram Group of Institutions, Tirupur. Abstract-Cloud-based outsourced storage relieves the client s burden for storage management and maintenance by providing a comparably low-cost, scalable, location-independent platform. However, the fact that clients no longer have physical possession of data indicates that they are facing a potentially formidable risk for missing or corrupted data. To avoid the security risks, audit services are critical to ensure the integrity and availability of outsourced data and to achieve digital forensics and credibility on cloud computing. Provable data possession (PDP), which is a cryptographic technique for verifying the integrity of data without retrieving it at an untrusted server, can be used to realize audit services. In this paper, profiting from the interactive zero-knowledge proof system, we address the construction of an interactive PDP protocol to prevent the fraudulence of proverb (soundness property) and the leakage of verified data (zero knowledge property). We prove that our construction holds these properties based on the computation Diffie Hellman assumption and the rewind able black-box knowledge extractor. We also propose an efficient mechanism with respect to probabilistic queries and periodic verification to reduce the audit costs per verification and implement abnormal detection timely. In addition, we present an efficient method for selecting an optimal parameter value to minimize computational overheads of cloud audit services. Our experimental results demonstrate the effectiveness of our approach. Index Terms Cloud computing, PDP, black box, extrato. I.INTRODUCTION Cloud computing, or something being in the cloud, is an expression used to describe a variety of different types of computing concepts that involve a large number of computers connected through a real-time communication network such as the Internet. In science, cloud computing is a synonym for distributed computing over a network and means the ability to run a program on many connected computers at the same time. The phrase is also more commonly used to refer to network-based services which appear to be provided by real server hardware, which in fact are served up by virtual hardware, simulated by software running on one or more real machines. Such virtual servers do not physically exist and can therefore be moved around and scaled up (or down) on the fly without affecting the end user arguably, rather like a cloud. The popularity of the term can be attributed to its use in marketing to sell hosted services in the sense of application service provisioning that run client server software on a remote location. 194 A. Outsourcing Fig.1 Architecture of Cloud Computing Cloud-based outsourced storage relieves the client s burden for storage management and maintenance by providing a comparably low-cost, scalable, locationindependent platform. However, the fact that clients no longer have physical possession of data indicates that they are facing a potentially formidable risk for missing or corrupted data. To avoid the security risks, audit services are critical to ensure the integrity and availability of outsourced data and to achieve digital forensics and credibility on cloud computing. Provable data possession (PDP), which is a cryptographic technique for verifying the integrity of data without retrieving it at an untrusted server, can be used to realize audit services. B. Provable data possession In this paper, profiting from the interactive zeroknowledge proof system, we address the construction of an interactive PDP protocol to prevent the fraudulence of proved (soundness property) and the leakage of verified data (zero knowledge property). We prove that our construction holds these properties based on the computation Diffie Hellman assumption and the rewind able black-box knowledge extractor. We also propose an efficient mechanism with respect to probabilistic queries and periodic verification to reduce the audit costs per verification and implement abnormal detection timely. In addition, we present an efficient method for selecting an optimal parameter value to minimize computational overheads of cloud audit services. Our experimental results demonstrate the effectiveness of our approach.

2 II.OBJECTIVE A. Advantages Objective of the project is to verify the integrity of data without retrieving it at an untrusted server and can be used to realize audit services and to achieve a privacypreserving public auditing system for cloud data storage security and Make TPA to perform multiple auditing tasks simultaneously.we also show how to extent our main scheme to support batch auditing for TPA upon delegations from multi-users. III. EXISTING SYSTEM Enterprises usually store data in internal storage and install firewalls to protect against intruders to access the data. With proven security relied on number theoretic assumptions are more desirable, whenever the user is not perfectly happy with trusting the security of the VM or the honesty of the technical staff. The challenging problem is how to effectively share encrypted data. The existing method is a new technique, which is based on tree structure in providing keys to the files. Each leaf node file has a secret key for decrypting that ciphertext class of that file. And the parent nodes also have a secret key. If the key is granted to receiver for the leaf node, that particular file only decrypted. And if the key is granted for the parent node, then the receiver have rights to decrypt the parent node and the leaf nodes which are under that parent node. The remaining node remains same. B. Disadvantages The disadvantages of existing system are 1. Not a full secure, because TPA (Third Party Audit) knows the all the details. That reason only the user privacy is affected. 2. TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user The advantages of proposed system are 1. Using cryptographic technique (PDP). 2. It with random mask technique to achieve aprivacypreserving public auditing system for cloud data storage security while keeping all above requirements in mind. 3. Security and performance is high. 4. Highly efficient. 5. Bilinear aggregate signature to extend our main result into a multiuser setting, where TPA can perform multiple auditing tasks simultaneously. B. Data Flow Diagram User request Audit outsourcing service system Monitoring TPA (Third party Audit) Secure and performance analysis 3. The third party auditing process should bring in no new vulnerabilities towards user data privacy IV.PROPOSED SYSTEM The proposed method called public Provable data possession (PDP), which is a cryptographic technique for verifying the integrity of data without retrieving it at an untrusted server; can be used to realize audit services. It with random mask technique to achieve a privacypreserving public auditing system for cloud data storage security while keeping all above requirements in mind. To support efficient Handling of multiple auditing tasks, we further explore he technique of bilinear aggregate signature to extend our main result into a multiuser setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient. We also show how to extent our main scheme to support batch auditing for TPA upon delegations from multi-users. 195 yes Yes No Fig.2Data Flow Diagram of efficient Audit Outsourcing System If validated User? Get Access Closed Not allow to access

3 C. System Architecture 4. Granted applications (GA) Who have the right to access and manipulate stored data. These Applications can be either inside clouds or outside clouds according to the Specific requirements. 2. Audit Outsourcing Service System: In this module the client (data owner) uses the secret key to preprocess the file, which consists of a collection of blocks, generates a set of public verification information that is stored in TPA, transmits the file and some verification tags to Cloud service provider CSP, and may delete its local copy.at a later time, using a protocol of proof of irretrievability, TPA (as an audit agent of clients) issues a challenge to audit (or check) the integrity and availability of the outsourced data in terms of the public verification information. It is necessary to give an alarm for abnormal events. 3. Secure and Performance Analysis: In this module, we considered to secure the data and give performance to the following: Fig.3 System Architecture of efficient Audit outsourcing system V. MODULE DESCRIPTION The following modules are used in proposed system 1. Audit Service System 2. Data Storage Service System 3. Audit Outsourcing Service System 4. Secure and Performance Analysis 5. Implementing KAE method in TPA: 1.Audit Service System: In this module we provide an efficient and secure cryptographic interactive audit scheme for public audit ability. We provide an efficient and secure Cryptographic interactive retains the soundness property and zero-knowledge property of proof systems. These two properties ensure that our scheme can not only prevent the deception and forgery of cloud storage providers, but also prevent the leakage of outsourced data in the process of verification. Data Storage Service System: In this module, we considered FOUR entities to store the data in secure, 1. Data owner (DO) Who has a large amount of data to be stored in the cloud. 2. Cloud service provider (CSP) Who provides data storage service and has enough storage spaces and Computation resources. 3. Third party auditor (TPA) Who has capabilities to manage or monitor outsourced data under the delegation of data owner. Audit-without-downloading To allow TPA (or other clients with the help of TPA) to verify the correctness of cloud data on demand without retrieving a copy of whole data or introducing additional on-line burden to the cloud users. Verification-correctness To ensure there exists no cheating CSP that can pass the audit from TPA without indeed storing users data intact. Privacy-preserving To ensure that there exists no way for TPA to derive users data from then information collected during the auditing process. High-performance To allow TPA to perform auditing with minimum overheads in storage, Communication and computation, and to support statistical audit sampling and Optimized audit schedule with a long enough period of time. 4. Implementing KAE method in TPA: A key-aggregate encryption scheme consists of five polynomial-time algorithms as follows. The data owner establishes the public system parameter via Setup and generates a public/mastersecretkeypair via KeyGen. Messages can be encrypted via Encrypt by anyone who also decides what cipher text class is associated with the plaintext message to be encrypted. The data owner can use the master-secret to generate an aggregate decryption key for a set of cipher text classes via Extract. The generated keys can be passed to delegates securely (via secure s or secure devices) finally; any user with an aggregate key can 196

4 decrypt any cipher text provided that the cipher text s class is contained in the aggregate key via Decrypt. VI.SCREENSHOTS A.Home Screen To registration Fig.4 Home Screen To Registration B. New user Registration Fig.5New user Registration C.Existing User upload the files Fig.6upload file 197 Fig.7successful upload VII.CONCLUSION AND FUTURE WORK In this paper, we addressed the construction of an efficient audit service for data integrity in clouds. Profiting from the standard interactive proof system, we proposed an interactive audit protocol to implement the audit service based on a third party auditor. In this audit service, the third party auditor, known as an agent of data owners, can issue a periodic verification to monitor the change of outsourced data by providing an optimized schedule. To realize the audit model, we only need to maintain the security of the third party auditor and deploy a lightweight daemon to execute the verification protocol. Hence, our technology can be easily adopted in a cloud computing environment to replace the traditional Hashbased solution. More importantly, we proposed and quantified a new audit approach based on probabilistic queries and periodic verification, as well as an optimization method of parameters of cloud audit services. This approach greatly reduces the workload on the storage servers, while still achieves the detection of servers misbehavior with a high probability. Our experiments clearly showed that our approach could minimize computation and communication overheads. In the future work, we can implement some more additional features in the same application.in future work, we utilize the public Provable data possession (PDP), which is a cryptographic technique for verifying the integrity of data without retrieving it at an untrusted server; can be used to realize audit services. It with random mask technique to achieve a privacy-preserving public auditing system for cloud data storage security while keeping all above requirements in mind. To support efficient Handling of multiple auditing tasks, we further explore he technique of bilinear aggregate signature to extend our main result into a multiuser setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly

5 efficient. We also show how to extent our main scheme to support batch auditing for TPA upon delegations from multi-users. VIII.REFERENCES [1] S. S. M. Chow, Y. J. He, L. C. K. Hui, and S.-M.Yiu, SPICE - Simple Privacy-Preserving Identity- Management for Cloud Envi-ronment, in Applied Cryptography and Network Security ACNS 2012, ser. LNCS, vol Springer, 2012, pp [2] L. Hardesty, Secure computers aren t so secure, MIT press, 2009, [3] C. Wang, S. S. M. Chow, Q. Wang, K. Ren, and W. Lou, Privacy- Preserving Public Auditing for Secure Cloud Storage, IEEE Trans. Computers, vol. 62, no. 2, pp , [7] M. J. Atallah, M. Blanton, N. Fazio, and K. B. Frikken, Dynamic and Efficient Key Management for Access Hierarchies, ACM Transactions on Information and System Security (TISSEC), vol. 12, no. 3, [8] J. Benaloh, M. Chase, E. Horvitz, and K. Lauter, Patient Controlled Encryption: Ensuring Privacy of Electronic Medical Records, in Proceedings of ACM Workshop on Cloud Computing Security (CCSW 09). ACM, 2009, pp [9] F. Guo, Y. Mu, Z. Chen, and L. Xu, Multi-Identity Single-Key Decryption without Random Oracles, in Proceedings of Informa- tion Security and Cryptology (Inscrypt 07), ser. LNCS, vol Springer, 2007, pp [4] B. Wang, S. S. M. Chow, M. Li, and H. Li, Storing Shared Data on the Cloud via Security-Mediator, in International Conference on Distributed Computing Systems - ICDCS IEEE, [5] S. S. M. Chow, C.-K. Chu, X. Huang, J. Zhou, and R. H. Deng, Dynamic Secure Cloud Storage with Provenance, in Cryptog-raphy and Security: From Theory to Applications - Essays Dedicated to Jean- Jacques Quisquater on the Occasion of His 65th Birthday, ser. LNCS, vol Springer, 2012, pp [6] D. Boneh, C. Gentry, B. Lynn, and H. Shacham, Aggregate and Verifiably Encrypted Signatures from Bilinear Maps, in Proceedings of Advances in Cryptology - EUROCRYPT 03, ser. LNCS, vol Springer, 2003, pp AUTHORS BIOGRAPHY K.Saranya received her B.E degree in Tamil Nadu college of Engineering, Coimbatore India and currently pursuing M.E degree in Jay Shriram Group of Institutions, Tiruppur, India. Her research interests include Cloud Computing, Data mining and Networking. System Dr.S. Rajalakshmi received her B.E. degree in Periyar University, Salem, India and M.E. degree n Anna University, Chennai, India and Ph.D. in Data mining in Anna University, India. Currently she is working as an Associate Professor in Jay Shriram Group of Institutions, Tirupur, India. Her research interests include Data mining, Cloud Computing and Big Data. 198

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